Semantic Information for Robot Navigation: A Survey

Abstract

There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstraction in the representation of information. In contrast with the early years, when navigation relied on geometric navigators that interpreted the environment as a series of accessible areas or later developments that led to the use of graph theory, semantic information has moved robot navigation one step further. This work presents a survey on the concepts, methodologies and techniques that allow including semantic information in robot navigation systems. The techniques involved have to deal with a range of tasks from modelling the environment and building a semantic map, to including methods to learn new concepts and the representation of the knowledge acquired, in many cases through interaction with users. As understanding the environment is essential to achieve high-level navigation, this paper reviews techniques for acquisition of semantic information, paying attention to the two main groups: human-assisted and autonomous techniques. Some state-of-the-art semantic knowledge representations are also studied, including ontologies, cognitive maps and semantic maps. All of this leads to a recent concept, semantic navigation, which integrates the previous topics to generate high-level navigation systems able to deal with real-world complex situations.

1. Introduction

In recent years, there is a growing interest in adding high-level information to several robotics applications to achieve more capable robots, even able to react to unforeseen events. Following this trend, the mobile robotics field is starting to include semantic information to navigation tasks, leading to a new concept: semantic navigation. This type of navigation brings closer the human way of understanding the environment with how the robots understand it, providing the meanings to represent the explored environment in a human-friendly way [1].

People identify the place where they are both spatially and conceptually. If a person moving through an indoor environment is asked about the trajectory of their displacement, they will not answer in terms of nodes or coordinates. Instead, people tend to provide concepts such as “I was in the living room and I went to the kitchen to drink a glass of water”. In this regard, there are efforts towards including semantic concepts of the environment in robot navigation. This can be achieved by implementing cognitive maps, an approach able to encode information about the relationships between concepts in the environment. Concepts are high-level (abstract) entities that group objects (e.g., chair, table, bed) and places (e.g., bedroom, office, kitchen), utilities (e.g., used for sitting) and the relationships between them. Objects and places detected in the environment are translated into physical entities. These entities can also be related to concepts, such as the utility of an entity. This kind of representation allows robots to build maps that can be understood by humans, thus reducing the gap between geometric interpretation and high-level concepts.

Semantic maps provide a representation of the environment considering elements with high-level of abstraction [2]. These possess different meanings for humans including the relationships with spatial elements used in low-level navigation systems. Semantic navigation requires connecting the high-level attributes with the geometric information of the low-level metric map. This high-level information can be extracted from data coming from a range of sensors, allowing the identification of places or objects. Adding semantic meaning to the elements in the scene and their relations also facilitates human-robot interaction (HRI) since the robot will be able to understand high-level orders associated with human concepts. For instance, Kollar and Roy applied these concepts to understanding natural language interactions when a person requests a robot to find a new object and the robot must look for that object in the environment [3]. Therefore, adding semantic knowledge in mobile robots navigation tasks poses an important advance with respect to traditional navigation techniques that generally use metric [4,5,6], topological [7,8,9], or hybrid maps (a combination of the previous two ones) [10,11].

When adding this high-level information to navigation systems, the first issue to address is how to represent knowledge. In this regard, Ontologies are one of the best ways of obtaining high-level representations of knowledge through hierarchies of concepts [12]. Since commonly concepts of entities, such as objects, tend to be separated from physical and real objects in the environment, spatial and conceptual hierarchies arise. Galindo et al., propose to make two categories of hierarchies [13]. The spatial hierarchy contains metric information of the environment, and its nodes represent open areas such as rooms or corridors. The arcs represent the possibility of navigating from one node to another. The conceptual hierarchy models semantic knowledge from environment information. In this approach, all concepts derive from the Thing entity. This is the highest-level concept from which the rest of concepts in this architecture derives. The hierarchy has three levels, with Thing in the first one. On the second level, there are the entities Room and Object, and the next level contains specifications of those concepts (kitchen, bedroom, bed). Figure 1 depicts the content of the three levels in the conceptual hierarchy and how this is related to the spatial one. The spatial hierarchy includes the abstract node that encodes all knowledge at the top level, the environment topology is placed at the middle level and the sensory information, such as images or gridmaps, is located at the bottom level. This knowledge is considered when creating semantic map. These maps are representations of the environments enhanced with information associated with objects, known places, actions that could take place, etc. Also, notice that semantic navigation requires that the robot integrates several skills. In this type of navigation, the communication between the robot and the human user acquires greater relevance since high-level commands issued by the user can be understood directly by the robot. For this reason, a dialogue capability for the human-robot interaction is recommendable. Another skill the robot must have is the ability to detect elements of the environment, allowing the robot to make a classification of rooms.

Figure 1. The spatial and semantic information hierarchies [13].

Summarising, semantic navigation is a paradigm that integrates high-level concepts from the environment (objects or places) in a navigation stack. Relationships between concepts are also exploited such as the most likely places that contain certain kinds of objects, or the actions that can be performed with determined objects. With the knowledge generated from these concepts and relationships, a robot can perform inferences about the environment that can be integrated into navigation tasks, allowing a better localization or planning to decide where not detected objects could be located. In this regard, semantic navigation brings significant benefits to mobile robot navigation.

• Human-friendly models. The robot models the environment with the same concepts that humans understand.
• Autonomy. The robot is able to draw its own conclusions about the place to which it has to go.
• Robustness. The robot is able to complete missing information, such as failures by detecting objects.
• Improves the location: The robot constantly perceives elements congruent with the knowledge about its location. For example, if it perceives a sofa, it confirms that it is in a living room.
• Efficiency: When calculating a route, you do not need to explore the entire environment. It is possible to focus on a specific area for partial exploration.

The rest of this paper is organized as follows. Section 2 presents different mechanisms for semantic information acquisition in robot navigation applications, focusing on whether the acquisition is fully autonomous or assisted by a human. After acquiring the information, it is important to study formalisms to represent and handle that knowledge. Section 3 reviews the main trends in knowledge representation. The high-level (semantic) knowledge allows for richer navigation. In this regard, Section 4 explores the principles of semantic navigation, describing the main elements that constitute a semantic navigator. The current study raised a series of open issues that are reviewed in Section 5 and, finally, Section 6 reviews the main contributions of this work.

2. Acquisition of Semantic Information in Robot Navigation

One of the tasks to solve in the navigation of mobile robots is the acquisition of information from the environment. In the field of semantic navigation, information includes concepts such as objects, utilities or room types. The robot needs to learn the relationships that exist between concepts included in the knowledge representation model. The existing techniques that address this task can be grouped into two categories: those that allow the robot to acquire information through the assistance of a human, and those that focus on the autonomous acquisition by the robot.

2.1. Human-Assisted Information Acquisition

Recognizing objects and places is a difficult task for robots. For this reason, some techniques involve users in the process of object identification to create augmented maps. In these cases, robots can use multimodal interaction (e.g., through voice, tactile screens, computer vision or keyboards) [14]. Zender et al., presented a system able to create conceptual representations of indoor environments [15]. In this case, the robot had previous knowledge about spatial concepts, and the role of the user was to assist the robot in the process of labelling the places. In Zender et al., the robot communicated through utterances. While walking with the robot, the user expressed what he/she considered relevant, providing instructions such as You are in the hallway or This is the charging station. The work of Nieto-Granda et al., describes something similar: A user acted as a guide to assist the robot in the process of associating spatial regions with semantic labels [16]. Pronobis and Jensfelt presented a multi-layer semantic mapping algorithm that combines information about the presence of objects and the semantic properties of the space (room size, appearance, etc.) [17]. With this information the system performs a classification of rooms, integrating data provided by the user as additional properties on the existing objects. Finally, the user provided information about the existence of an object, and the robot treated it as another source of information. In the approach presented by Petalson et al., the robot learns places through interaction with the user [18]. This work did not consider adding objects to the semantic map. In contrast, the work of Crespo et al., implemented dialogues in natural language between users and the robot through voice and keyboard interfaces [19]. This system was able to include objects in the map as well as their semantic relations. For instance, the robot asked the user about the possible uses of an object or the interaction possibilities with objects in the environment. An extension of this system is explored in Barber et al. [20]. This version addresses the problem of path planning. If the destination is a known object or place, the system solves a classic navigation problem towards the position of the object or place. Conversely, if the destination or goal is an unknown place or object, the system starts an inference process based on the relationships between objects. For instance, if the goal is drinking something cold, the system selects cold water as a valid objective. The inference system relates cold water to the refrigerator, which is related to the kitchen. Then, the system decided to navigate to the kitchen. Kruijiff et al., introduced a system that aims at improving the mapping with explanatory dialogues between human and robot using natural language [21]. Similarly, Hemachandra et al., presented a system to associate spatial and semantic information. This was done with the user and robot moving and interacting together through a path [22].

2.2. Fully Automated Information Acquisition

Apart from the previous approaches that rely on information coming from interaction with humans, other works try to deal with the problem of information acquisition autonomously. Gemignani et al., divided these methods into three categories [23]. The first one includes those methods that acquire characteristics from the environment using metric data from laser sensors to obtain high-level information. Galindo et al., presented a system that represents knowledge of the environment as an augmented topological map with semantic knowledge through a linkage called anchoring [13]. The second category includes techniques that use classification and clustering for automatic segmentation and labelling of metric maps. In this category, we can find the works proposed by Goerke and Braun [24], Brunskill et al. [25], and Friedman et al. [26], using Adaboost, spectral clustering and random fields of Voronoi, respectively, to generate two-dimensional topological maps. The third category deals with object recognition and place labelling using visual cues. The work of Wu et al., tackled the problem of visual-spatial categorization for mobile robots [27]. The system predicted the semantic category of a place based on images, distinguishing between places such as kitchen, living room, etc, integrating system of visual characteristics called CENTRIST (census transform histogram). Tian et al., combined range data with images to create the cognitive map and localize the robot [28]. The depth information improved the map created. Authors claimed that the depth information of the RGB-D sensor has significantly improved the loop closure as well as feature matching, and that provides a better spatial cognitive map. Apart from these three categories, other authors proposed adding a fourth one that tries to classify places using information about the actions that people perform in the environment [29].

2.2.1. Understanding the Environment

High-level navigation systems need rich information to generate representations of the environment. Object recognition, people detection and classification techniques are therefore one of the pillars on which a semantic navigation is founded. Although exploring these techniques is not the main focus of this work, they are necessary to understand better how semantic navigators work.

Object and people detection

Semantic navigators integrate methods for object detection such as SIFT [30] combined with other methods used in [31], [32] or [33]. More specifically, the work of Ekwal et al., proposed a mobile robot that autonomously navigates in a domestic environment [31]. This work pursued integrating spatial and semantic knowledge in a service robot, putting together SLAM and an object detection system to generate a high-level representation of the environment. The work presented in Ekvall and Kragic describes a computer vision system that is part of an autonomous robot system that performs pick-and-place tasks using programming by demonstration for automatic interpretation of the teacher’s instructions [32]. The work of Lopez aimed at enabling a mobile robot, with a camera and a laser sensor, to navigate in an environment while looking for known objects [33]. The development of a system of active visual search [34], combining semantic tracks to guide the process of object search is also interesting. The proposal starts from an unknown environment and implements an exploration strategy that takes into account the task of finding an object. The planner adapts the behaviour of the search depending on the current conditions using an indirect search algorithm [35] (e.g., when looking for a cellphone, the robot will look for a table first as it it likely that a phone can be on top of it).

The ability to detect and identify objects is often used in the task of places classification required by the semantic navigation. Mozos et al., presented a system of recognition of furniture to accomplish this task [36]. Semantic navigation, therefore, can get a direct benefit from place classification methods that allow finding the location of a particular object. Joho and Burgard considered the problem of how background knowledge about the location of usual objects can be used to find an object in an unknown environment [37]. Astua et al., proposed a system that allows a mobile robot to differentiate among objects in a scene and use that information in semantic navigation [38]. The requirements of objects detection in this field are less than those applied to handling and gripping objects [39] in terms of the precision of location. However, there are works where objects and their distances are used for the classification of places [40] or for assessing the presence of objects in a location to make inferences [41].

A recent trend in object detection is semantic segmentation, a paradigm that assigns a class label to the pixels in an input image [42]. To achieve this, classifiers such as Pyramid scene parsing network (PSPNet) [43] are integrated on the approach for guiding a robot autonomously relying only on visual information. Another work, proposed using semantic segmentation and detection masks as observations and use deep learning to learn navigation policies [44]. The proposal uses high-level semantic and contextual features in the segmentation process coupled with detection masks. Also, this system takes advantage from the fact that once an agent has been trained in similar environment, it can discover commonly encountered objects and contextual cues, which allows learning policies that are able to generalize to unseen environments.

Together with object detection, the detection of people is a problem widely addressed. A complete system can be found in Luo et al. [45] where authors performed bulk segmentation, detection of head and shoulders and a time refinement. Additionally, the people detection algorithm used in Crespo et al. [29] included the detection of legs proposed by Aguirre et al. [46]. The diagram of the system is represented in Figure 2. The approach gets data from the room noise using microphones connected to an Arduino and the movement of people in the environment through the detection of legs. By combining data on a Support Vector Machine (SVM), the system is able to differentiate different types of rooms. A recent work exploits other kind of environmental data, combining information coming from an artificial nose with visual information [47]. By doing so, the system is able to detect objects that emit certain kinds of gas. Authors argue that this system could be useful in certain indoor environments where information associated with these kinds of objects may help exploiting new semantic relationships.

Figure 2. Office 3D semantic map renderings [29].

Labelling

In the last decades, a number of researchers have proposed methods to label rooms or areas where the robot can move. Labelling consists of naming such room or region to uniquely identify it with respect to other regions, and also to attach it to a category that provides certain properties that can be taken into account in the navigation process. The information provided by each category depends on the level of abstraction allowed by the detection and classification process and the ontology used. This means that the perception capabilities of the robot are directly related to the level of abstraction provided by the labelling process. Kostavelis and Gasteratos faced the problem of semantic navigation by breaking it down into discrete tasks [48]. This work addressed the objectives of place recognition and region categorization. The navigator uses machine learning techniques to manage dynamic changes in the explored environment and this work assumes that the robot should be able to classify and label all places. The semantic navigator also uses information related to the category of the place. The categories that can be handled depends on the complexity of the system. The categories can be therefore simplified to room, hall, door or can model more complex information, leading to more detailed representations such as kitchen, living room, bedroom for defining rooms. In any case, the cognitive navigator relates categories to regions of the environment.

Literature on methods of labelling places for robot navigation is wide. Drouilly et al., discussed the differences between labelling in indoor places and outdoor ones since in the latter usually there is no clear separation between different places [49]. This work uses RGB-D information for semantic mapping, including a labelling layer that relies on a Random Forest classifier. Cleveland et al., presented an automatic robotic system that generates semantic maps in retail environments using point clouds to recognize and label objects [50]. From the 3D information, the system uses SIFT features to perform classification.

In labelling, there is a trend that intend to identify regions of interest in the environment, such as floor, walls or doors [51]. But, although this endows the system with some knowledge regarding navigation, it does not categorize the place. This task was addressed in Shi et al. [52] where the system differentiates between halls, offices, reading rooms and doors. Besides, the authors weighted the advantages and disadvantages of using different sensors for the semantic labelling of places. Another work used Scale-invariant feature transform (SIFT) [53] features to characterize different areas [54]. This information was combined with a probabilistic description logic. Hernandez et al., presented a preliminary proposal for a toposemantic navigation model based on visual information for indoor environments [55]. This system relies on region detection at the lowest level and use this information to grow on abstraction, adding information from objects detected in the scene. A recent work proposed a vision-based three-layer perception framework based on transfer learning for mobile robots during semantic navigation [56]. This proposal includes a place recognition model to distinguish different regions in rooms and corridors.

Apart from those systems that perform object recognition and classification using visual information, the work of Mozos et al., proposed using laser range-finder measurements for semantic labelling of places [57]. Observations are classified by applying a sequence of binary classifiers based on AdaBoost. Sousa et al., adapt techniques normally used in computer vision to laser range measures to distinguish between rooms and corridors using SVM [58]. Additionally, a labelling approach based on a multi-sensory system was described in Pronobis et al. [59]. This system combine visual cues with laser range data with a SVM classifier to distinguish between indoor environments (corridor, office and meeting room).

At this point, it should be clear that mobile robot navigation can benefit from adding high-level semantic knowledge. Topological navigators can be generated from the results of classifiers labelling the nodes of the map. Also, semantic navigators that use the methods presented in this work can be built on a topological or geometric navigator. Mozos et al., used data from a system that returns a plane of 360 degrees to distinguish between room, hallway, door and entrance hall [60]. To achieve this, the system only used geometric data. In contrast, many proposals use computer vision to detect and recognize objects for place labelling. For instance, Rottmann et al., used Haar features to get the number of specific objects present in the environment to perform Semantic Place Classification of Indoor Environments With Mobile Robots [61].

Different categorizations of places were discussed in Charalampous et al. [62], dividing the classifications of indoor places into two types:

• Indoor single scene interpretation. Different works converge here. Mueller and Behnke proposed a framework to perform semantic annotations of RGB-D data [63]. Authors used the implementation of a Random Forests classifier to group the scenes, and SVM to predict objects and indoor scenes. The model is based on Conditional Random Field (CRF) to provide unary features. Another approach that is also supported by CRF and Random Forests is presented in Wolf et al. [64], while in Gutierrez-Gomez et al. [65] authors proposed segmenting the scene into fragments of neighbouring 3D points. Having in mind low-level features such as textures or normal entropy, researchers manage to differentiate the areas of the scene that change over time from those that are static. This makes it easier to recognize places when the robot returns to visit a place.
• Indoor large scale interpretation. This category includes works such as the proposal of Hermans et al. that proposed a method for semantic segmentation of 3D scenes in different places [66]. The 3D reconstruction process is carried out by adding new scenes to those previously acquired. The locations are then labelled with distance, colour, and normal orientation information. Ranganathan, proposed an online method that segments RGB streams and labels inferring the parameters in a Bayesian model [67].

Besides, methods of place recognition supported by binary characteristics descriptors are suggested [68]. This represents a tendency to categorize places using statistical hypotheses. Mafra et al., proposed a place recognition system for UAV navigation that combines 2D and 3D information [69]. This approach codifies the information into a hierarchical Bag of Binary Words visual vocabulary. In Lu et al., authors presented a framework that includes cost maps in layers, each one of these maps has a distinctive semantic meaning [70]. However, it is quite widespread that researchers join topological or geometric maps with data from objects located in the environment. Wong et al., combined a metric map with objects in space [71]. Something similar was proposed by Zhao and Chen [72]. This work introduced a method that combines SLAM with an object labelling system. In this line is also the work of Deeken et al., that presented a framework called SEMAP [73]. This approach was developed in ROS to store and manipulate objects on a semantic map.

Table 1 shows the trends of different works regarding the acquisition of information from the environment in semantic navigators. This table shows which works use a human assistant to label the place where the robot is located. Besides, the table provides information about what works also allow a human assistant to interact with the robot so that it learns the objects that surround it and/or the relationships between those objects with other elements of the environment. Regarding the acquisition of information through the sensors of the robot, the table shows which works employ a processing of the information received by laser range data. It also takes into account who applies grid map segmentation techniques. Also, it is shown what authors use the information of the objects detected by the robot to acquire relevant information to label the place where it is located. It is worth noticing that some works follow a hybrid scheme, using both information acquisition through interaction with humans as well as autonomous information acquisition processes.

Table 1. Comparison between information acquisition methods.

	Acquisition by Human Interaction		Autonomous Acquisition
Paper	Place Labeling	Learning Objects and Relationships	Laser Range Data	Segmentation	Object Detection
[19]	YES	YES (BOTH)	NO	NO	YES
[15]	YES	YES (BOTH)	YES	YES	YES
[16]	YES	NO	YES	NO	YES
[17]	YES	YES (OBJECTS)	YES	YES	YES
[74]	YES	NO	NO	NO	NO
[75]	YES	NO	NO	NO	NO
[21]	YES	DOORS	NO	NO	NO
[23]	NO	YES (OBJECTS)	NO	YES	YES
[13]	NO	NO	YES	NO	YES
[24]	NO	NO	YES	NO	NO
[25]	NO	NO	YES	YES	NO
[28]	NO	NO	YES	NO	YES

Crespo et al., included techniques designed to recognize patterns in the actions of individuals to label a room, that is, labelling a place depending on what people do in them [29]. Kollar and Roy relied on probabilistic methods based on Naive Bayes for the classification of places, obtaining the probabilities of objects locations [3]. It is a fact that some objects tend to be or belong in certain types of places in the environment. Object-object relations (for example, a sofa can be near a remote control and vice versa) and object-scenario (a sofa, a remote control, and a TV are related to a living room) can be useful to predict the location of a great variety of objects and scenarios. Another work also introduced a Bayesian approach for semantic mapping [76]. This proposal combined a semantic, topological and geometrical mapping of the space and nodes relative to objects. The task of localization included information from methods of place classification.

3. Representation of Semantic Knowledge

The literature offers different ways of representing high-level knowledge in navigation tasks. Semantic navigation approaches builds upon some of these knowledge representation paradigms. This is the case of Pronobis and Jensfelt that propose a spatial representation in a layered structure [17] as shown in Figure 3. This representation can describe usual knowledge as relationships between concepts (e.g., a kitchen contains-the-object cereals). Additionally, instances of knowledge are described as relations between instances of concepts (object-1 is-an-instance-of cereals), or relations between instances and other instances (place X has–the-object object Y). Relationships in the conceptual map can be predefined, acquired or inferred. These relations can also be deterministic or probabilistic (modelling uncertainty). This semantic navigation system bases inference on deductions on the unexplored space, such as predicting the presence of objects in a location with a known category. Additionally, this system is able to predict the presence of a room in unexplored space. Adding features to objects can also provide useful information since they can modify the functionalities of the objects [19]. For instance, a broken chair may not be used for sitting and be located in a workshop instead of in a living room.

Figure 3. Spatial representation in a layered structure [17]. The conceptual layer contains concepts knowledge, relationships between concepts and spatial entities instances.

Galindo et al., presented one of the first works in the semantic navigation field, introducing multi-hierarchical models of representation [13]. This work proposed NeoClassic as a tool for representing knowledge and Descriptive Logics (DL) as inference mechanism [77]. The inputs to the classification system are outlines and, therefore, simple volumes, such as boxes or cylinders, represent furniture.

Some authors include features of the environment such as the sound level or the number of people in each room [78]. Gaps authors left in their conclusions haven been covered, such as the problem of autonomously learning new properties and categories of rooms by the robot, heading to an auto extensible semantic mapping. Ruiz-Sarmiento et al., introduce a novel representation of a semantic map called Multiversal Semantic Map [79]. The authors provide measures of uncertainty when they categorize an object into degrees of belief. An object can be labelled as a microwave with a certainty of 0.6 and as a bedside table with 0.4, for example. This type of semantic map contains all the categories considering all the possibilities. The work of Galindo et al. [13] was extended and different combinations of possible bases or universes were considered, such as instances of ontologies [80] with annotations of belief (certainty) about the concepts and relationships that are useful fundamentals.

The potential of this representation was extended in subsequent works that implemented semantic navigation schemes. For example, the planner was improved in Galindo et al. [81] and a more recent work includes autonomous generation of objectives [82]. The work of Zender et al., follows a similar line but, in this case, the multi-hierarquical representation is replaced by a simple hierarchy [15]. This is achieved by moving the map data from sensors to conceptual abstractions. The tool selected to code this information was Ontology Web Language-Description Logic (OWL-DL), resulting in an ontology that defined an office domain.

Other authors, such as Nüchter and Hertzberg, use Prolog to implement networks of constraints that code properties and relations between the different flat surfaces of a building (walls, floor, ceiling and doors) [83]. The classification is based on two techniques: contour detection and a cascade of classifiers that use distance and reflectivity data.

Other acquisition systems, representation systems and use of semantic maps can also be found. One example is KnowRob-Map by Tenorth et al., where Bayesian logics networks are used to predict the location of objects according to their relationships [84]. All of this is implemented in SWI-Prolog and using an OWL-DL ontology. They use networks with Bayesian logic to predict where an object can be (within the semantic environment) based on their relationships. For example, if a knife is used to cut meat, meat is cut for cooking and the kitchen is the place to cook, the knife can be in the kitchen. In contrast, The ontology of concepts that represents semantic knowledge can be seen as lists implemented using a database scheme [19] as shown in Figure 4. The reasoning can be performed as queries to the database. Alternatively, the ontology can be translated into lists of facts and rules and inferences can be made through a reasoning engine such as NeoClassic [13].

Figure 4. Knowledge representation on concept lists linked each other [19].

From these works, ontologies can be considered as the tool to formalize semantic knowledge.

In robots navigation tasks, the way of mapping the environment is an essential stage. The type of map for the navigator depends on the abstraction level, resulting in different kinds of navigation. In a geometric navigator, the required mapping information corresponds to distances from the robot sensors [85]. The map in these approaches aims to distinguish those zones in the space corresponding to obstacles from the accessible areas. In contrast, topological navigators use the connectivity between different areas to model the environment [86]. This allows building a tree-like space representation to calculate routes. Pronobis and Jensfelt added one extra level of abstraction, performing what they call semantic mapping [17]. This work considers applications where the robot moves in domestic or office environments, created by and for humans. Concepts such as rooms, objects and properties (such as the size and shape of the rooms) are important in the tasks of representing knowledge and generate efficient behaviours in the robot.

3.1. Ontologies

Ontologies are formal tools to describe objects, properties, and relationships in a knowledge domain. According to Prestes et al., two definitions capture the essence of the purpose and scope of ontologies [87]: On the one hand, Studer et al., building on the initial works of Gruber et al. [12], stated that an ontology is “an explicit formal specification of a shared conceptualization” [88]. On the other hand, the work of Guarino considered an ontology as a series of “logical theories that explain what a vocabulary tries to transmit” [89]. Therefore, we can establish that an ontology is constituted by a set of terms and their definitions as shared by a given community, formally specified in a language readable by a machine, such as first-order logic. More specifically, in the field of robotics, ontologies for representing knowledge are useful as they allow building models of the environment in which relevant concepts are hierarchically related to each other. In general terms, ontologies are composed by classes, which represent concepts at all levels, relations, which represent associations between concepts, and formal axioms, which are restrictions or rules that provide consistency to the relationships [90].

Even though ontologies have in common these elements, there are different ways to classify and differentiate them. Prestes et al., group ontologies by their level of generality, separating them in four kinds [87]. (i) Top-level ontologies describe general concepts such as space, time, objects, events, actions, etc. This generality makes them suitable for different domains. (ii) Domain ontologies describe concepts oriented to solve different problems if they are in a specific domain. Concepts relative to a domain ontology for a home environment could be a living room, a kitchen, a couch, sitting, etc. (iii)Task ontologies describe tasks or generic activities (e.g., grab something). And, finally, (iv) application ontologies are associated with one particular domain and to one task. task (e.g., fry an egg).

3.2. Cognitive Maps

When building cognitive maps, knowledge is extracted from present and historical information (previous knowledge), imitating the mechanisms of the human brain to solve complex cognitive problems in a flexible way [91]. This study concluded that, when asking people to describe concepts related to places (living room, kitchen, etc.), the definitions were usually built using the objects these places contain. Following these ideas, Milford et al., presented RatSLAM, a computational model of the hippocampus of rodents developed with Continuous Attractor Networks (CAN) of three dimensions that translate the robot position of the robot in the position of cells [92]. Shim et al., presented a mobile robot that uses a cognitive map for navigation tasks [93]. The map is built using a RatSLAM approach with an RGB-D sensor [28] which added depth information, invariable to lighting conditions. The cognitive map generated by its version of the RGB-D-based cognitive mapping algorithm is shown in Figure 5.

Figure 5. Cognitive map generated with a version of the cognitive map construction algorithm based on RGB-D [93].

Cognitive maps have been applied in scene recognition. For example, Rebai et al., presented an approach for indoor navigation that builds a visual memory that allows spatial recognition without storing visual information [94]. This approach builds the visual memory using Fuzzy ART, a model capable of rapid stable leaning of recognition categories in response to arbitrary sequences [95]. Authors propose this idea as a way of imitating the biological processes that encode spatial knowledge, mimicking how animals recognize previously visited places through cognitive maps. To achieve this, data association mechanisms are implemented to describe the robot environment that allows recognizing places already visited [96]. The system consists of constructing an incremental visual memory using Fuzzy ART and the visual features are used as input signals to create a visual cell representing the perceived scene (see Figure 6). In addition to this a bio-inspired process of Visual attention (VATT) is obtained, consisting of processing a certain part of the scene with more emphasis, while the rest is dismissed or suppressed.

Figure 6. Cognitive map modules based on visual memory [94].

The authors also indicate that this idea is exploited in the construction of cognitive maps and recognition of places using visual localization [97], besides being applied in navigation of mobile robots. At these points is where it makes sense to study it for this paper.

3.3. Semantic Maps

Semantic mapping is the map that takes into account complex semantic concepts of the environment, such as objects and their usefulness, different types of rooms and their uses, subjective sensations transmitted by a place in a user, etc. These concepts are related to each other offering valuable information about the environment that is reflected in the semantic map.

Being aware of the implications of the choice of technology and the level of abstraction to be used in the mapping, Wu et al., considered of great importance the application that has the mapping in service robots and present a hybrid model of the semantic map [98]. Classifying the navigation maps into two categories: the metric maps and the topological maps. To reach a map with the advantages of both categories and reducing the limitations, the hybrid maps [99] outstand. All belong to traditional maps and focus on representing the geometric structure of space, describing the quantitative coordinates and connectivity between locations. However, the functionality of the locations and the complexity of the partial space are not considered. Nor are high-level semantic concepts used to interact with people. This situation where the robot planner, location and navigation based on these types of maps do not meet the needs of the robot’s service tasks, motivated the definition of semantic maps [98], as a more suitable option than cognitive maps.

The paradigm of the development of semantic maps used by Pronobis and Jensfelt is based on space properties [17]. These properties can be described as attributes that characterize discrete space entities identified by the robot, such as places, rooms, or locations. Additionally, the properties make them correspond with human concepts and provide another layer of spatial semantics shared between the user and the robot. Each property is connected to a sensory information model. High-level concepts, such as room types, are defined by properties.

• Objects. Each object class corresponds to a property associated with the place. A particular location is expected to display a certain number of a particular type of object, and a certain amount of them is observed.
• Door. Determines whether a location is determined by a door.
• Shape. The geometric form of the room extracted from the information of laser sensors.
• Size. The relative size of the room extracted from the sensory information of the laser.
• Appearance. The visual appearance of a place.
• Associated space. The amount of free space visible around a placeholder not assigned to any place.

Conceptual map is shown in Figure 7. Every specific instance of the room is represented by a set of random variables, one for each property associated with that place.

Figure 7. Structure of conceptual map graph model [17].

Entering into concepts of creation of semantic maps (without being the same as a cognitive map), some works explore this concept and its application in mobile robotics. Nuechter and Hertzberg proved that semantic knowledge can help a robot in its task of reaching a destination [83]. And part of this knowledge has to be due to objects, utilities, events or relationships in the robot environment. The data structure that supports space-related information about this environment is the map. A semantic map increases the typical geometric and/or topological maps with information about entities (objects, functionalities, etc.) that are located in the space. This implies the need to add some mechanism of reasoning with some previous knowledge. In this way, a semantic map definition is reached:

A semantic map in a mobile robot is a map that contains, in addition to spatial information about the environment, assignments of mapped features of known class entities. In addition to the knowledge of these entities, regardless of the content of the map, some kind of knowledge base must be available with an associated reasoning engine for inferences [83].

The differentiation of elements of the environment is called as semantic mapping by many authors, also in outdoor navigation works. Li et al., they differentiate in the image that perceived from the environment what is road, sidewalk, wall, terrain, vegetation, traffic sign, pole and car [100]. Returning to indoor navigation, in the case of work that has already been mentioned [83], the authors focused on differentiating the following elements from the environment: wall, door, floor and ceiling. Rules were created using Prolog to implement the restrictions of the characteristics that differentiate the classes, being these purely visual. In Figure 8 the network of restrictions that defined these elements is shown, while Figure 9 shows the result of the object detection such as the fire extinguisher, a printer and an indoor tree in a flowerpot. The low-level mapping was a 3D SLAM.

Figure 8. Contrains for a scene interpretation [83].

Figure 9. Rendered image for an office 3D semantic map [83].

Once the image was rendered, a classification was carried out considering the outline of the objects apart from the depth and camera images. Then, the points corresponding to the 2D projection of the object (ray tracing) were chosen. Then it was paired with a 3D model at those points, followed by an evaluation step.

Nieto-Granda et al., also implemented a semantic map, responding to the need to classify regions of space [16]. Methods for automatically recognizing and classifying spaces are presented. Separating semantic regions and using that information to generate a topological map of an environment. The construction of the semantic map is done with a human guide. The partition of this map provides a probabilistic classification of the metric map, assigning labels, leaving for a future work the automatic assignment of these labels (a topic later solved in other works such as the proposal of Crespo et al. [19]). The symbolic treatment of the map and a path taken by the planner is shown in Figure 10.

Figure 10. Rooms and dooways represented by Gaussian regions and the robot’s path to room 4 from room 9 [16].

For these authors, the literature of semantic mapping has focused on developing mapping techniques that work by supporting human interactions, so that the representation of space is shared. One of the strategies used is to represent the relationship between a place and the knowledge associated with it (including the functionality of the place and the location of the objectives). Unlike works like Mozos et al. [57], which construct a topological map on a geometrical map, provide a continuous classification of the geometric map in regions labelled semantically. This multi-variable distribution is modelled as a Gaussian model. Each spatial region is represented using one or more Gaussian in its geometric map coordinate framework.

Naik et al., recently proposed a graph-based semantic mapping approach for indoor robotic applications that builds on OpenStreetMap [101]. This work introduces models for basic indoor structures such as walls, doors, corridors, elevators, etc. The models allows querying for specific features, which allows discovering task-relevant features in the surrounding environment to improve the robot planner.

Kuipers proposed a quantitative and qualitative model of space knowledge on a large scale [102], based on multiple representations of interaction and serves as the basis for previous authors in the representation of relations of objects, actions and dependencies of the environment. On a smaller scale of space, the work of Beeson et al., providing a representation of the more specific working environment [103].

4. Semantic Navigation

Several approaches for mobile robot navigation systems appeared in the last decade, with semantic navigation gaining interest. The work of Zender et al., follows this idea presenting a system able to create conceptual representations based on different abstraction level maps [15]. This approach is grounded on two aspects widely studied in the field fo psychology: how humans adopt a hierarchical representation of spatial organizations [104,105], and how humans perform a categorization of spatial structures [106,107]. This system includes a laser range finder, a camera and voice interaction capabilities for HRI. Crespo et al., further developed this system by adding semantic knowledge to objects [108]. This resulted on a multi-layer conceptual spatial map as shown in Figure 11, where the lowest layer corresponds to the metric map (the goal is a coordinate or point in space) and is suitable for low-level navigation (geometric navigation) as it includes metric measurements of free space and obstacles. The next layer, consists of a navigation graph that is able to differentiate between kinds of areas (hall, room and entrance). The topological map use some reference elements of the environment (nodes) according to the connections between them (arches). And finally, the conceptual map put together a conceptual ontology and information obtained through dialogues with the users.

Figure 11. Multi-layer conceptual spatial map [108].

Semantic navigation allows mobile robots to perform a semantic planning. As an example, Sun et al., recently presented a planning system for indoor navigation and household tasks [109]. The tasks implemented are move to region, move obstacles, clear a region of objects and moving objects to another region. The robot is able to plan a path to an object using information from a semantic map instead of fixed coordinates.

4.1. Principles of Semantic Navigation

Approaches of semantic navigation follow some common principles, including a framework for topological mapping that includes geometrical information, adding topological abstraction. Integrating a lower level navigator is common in semantic navigation approaches. Examples can be found in the work of Mozos et al. [57], where authors describe how to generate a topological map, assuming that the robot has a priori map of the environment, using an occupation grid approach [110]. Following these ideas, Crespo et al., presented an interface to integrate different low-level navigators in a semantic navigation system [78].

The semantic navigation methods also integrate approaches for classification of places, or labelling, as studied in Section 2.2.1. Kostavelis et al., use a histogram-based classification method to classify indoor environments in university buildings (e.g., corridors, laboratories, meeting-rooms, offices, etc.) [111]. Another approach uses AdaBoost for classification of six indoor places (room, corridor, doorway, kitchen, office, seminar room and laboratory.) [57]. More recently, another work includes a place classification method based on people activities, resulting on the labelling of six environments (cafeteria, library, corridor, exhibition room, conference room and indoor soccer field) [29]. These approaches include a partition of unsupervised places, for instance segmenting the environment to have distinguishable places on top of a topological map [111], or creating an ontology that relates a conceptual hierarchy to the spatial hierarchy needed by a lower-level navigator [19]. Yang et al., include semantic knowledge into a Reinforcement Learning approach [112]. Authors use a graph representation to model knowledge and Graph Convolutional Networks [113] to handle relationships between features.

Finally, a conceptual representation of the detected places is necessary. In this sense, Kostavelis et al., proposed the use of augmented navigation graphs to relate the concepts of the conceptual hierarchy of places with each other [111]. Figure 12 shows how the nodes of the topological map (topometric) are matched with categories of the conceptual hierarchy. This conceptual hierarchy can also be expanded adding objects, utilities and subjective evaluations [108].

Figure 12. Augmented navigation graph and relations between mapping levels [111].

4.2. Elements of the Semantic Navigation

In recent decades, cognitive robotics applied to navigation has led to a combination of mobile robotics with a high level of perception. In this regard, semantic navigators involve two concepts [114]. The first one is related to the ability of the robot to self locate and to generate a metric map of the explored environment. The second one is semantic interpretation and it refers to the ability of the robot to understand its environment. Therefore, cognitive robots should be able to carry out semantic inferences based on mechanisms of interpretation of the context, even when places are visited for the first time [48].

The work of Kostavelis and Gasteratos points out the basics of a framework for cognitive (semantic) navigation, separating the problem into a set of tasks [48]. The first one involves low-level navigation, which includes geometric and numerical attributes that the robot uses for localization. Next, the high-level navigation is considered to deal with the cognitive attributes to perform semantic inferences about the robot’s location. This task can be separated in three sub-tasks: (i) Spatial abstraction, that deals with the representation of the spatial information learnt; (ii) Recognition of places, where the system recognizes instances of places to navigate from one location to another; and (iii) Classification of places, that allows generalizing, recognizing places even if some information is not available. Finally, an interface is necessary to connect the low-level with the high-level navigation modules.

Semantic navigation is gaining popularity, with recent works offering insights about current trends and future directions of this research area. Table 2 gathers the main features that define semantic navigation, paying special attention to the reasoning module. In this regard, the work of de Lucca Siqueira et al. [115] proposed behaviour trees and finite-state machine (FSM) to model the possible states of the agent and the transitions between these states [116]. Conversely, another work does not implement a reasoning module, adding instead semantic concepts to the planning task [117]. The work of Talbot et al., uses semantic knowledge to infer the destination modelling the environment as a physical system, treating spatial specifications as the mass in a mass-point system [118]. Under this assumption, spatial constraints are modelled as forces applied to a moving mass.

Table 2. Comparison between navigators with semantic knowledge.

Features
Ref	Reasoning System	Object Detection	Environments Elements Taken into Account	Place Classification	User Interaction	Learning Possibility
[115]	Behavior tree, FSM	Unused or unspecified	Weather, task location, energy source location, events, a room, a position, etc.	Environment context	Unused or unspecified	No
[119]	Unused or unspecified	A version of the Convolutional Neural Network (CNN)	Roads, buildings (red), road markers, lawns, vehicle, pedestrian, bike, sky, fence, pole, etc.	They classified 11 classes for example, Road and Tree	Unused or unspecified	Unused or unspecified
[118]	The used spatial constraints as a mass in a point-mass system	Unused or unspecified	Unused or unspecified	Unused or unspecified	Unused or unspecified	Unused or unspecified
[117]	Extracting useful navigation information from semantic roles	This paper use the NLPIR Chinese word segmentation system	Manual labeling	Speech Recognition Library (SRL)	Unused or unspecified
[15]	A description-logic reasoner	A component for detecting and following people [25] and a method based on [32]	Doorways, Corridors, rooms. Smaller objects, like cups, books, etc.	Based on functionality, it depends on the objects inside the area	Speech recognition	Yes
[111]	Unused or unspecified	Unused but they claim that they will use object recognition	The norm between the detected features and the visual words is calculated and the representative histogram is formed	A frame is acquired, converted into an appearance based histogram and the SVM infers the label of the place	Unused or unspecified	Unused or unspecified
[120]	Unused or unspecified	Extracting 2D straight segments as a basic primitive and using ground-hypothesis	Edges and environment images features	Unused or unspecified	Unused or unspecified	No
[103]	The Hybrid Spatial Semantic Hierarchy (HSSH) frame-work	Unused or unspecified	gateways and path fragments	Using gateways and path fragments, they formulate a criterion for detecting topological places	Clicking or drawing on the LPM display	No
[19]	A reasoning system based on a relational BD implementation	Object and label detection	Objects in the environment and labels	Classification according to room utility	The keyboard can be used. They also used a voice dialog system.	Yes
[49]	A semantic cost function that takes into account high-level image constraints	Unused or unspecified	It uses object position in images	Unused or unspecified	There is no interaction	Unused or unspecified
[79]	Unspecified	objects are characterized through geometric and appearance features	Objects	Depending on the objects that are anchored with types of room	Unused or unspecified	Several processes of obtaining semantic knowledge are analyzed but not used yet

Beeson et al., focused on inferring topological maps from instructions of routes using the Hybrid Spatial Semantic Hierarchy (HSSH) framework [103]. Another approach proposed descriptive logic as a reasoning system, allowing inferring new knowledge about the world [15]. Crespo et al., followed a similar line, proposing a system able of reasoning and making inferences about world concepts [19]. This work presents a reasoning system implemented following a relational database scheme, extending the inference capabilities of Zender et al. [15]. A different approach proposed a semantic high-level scene interpretation mechanism to select the optimal path towards a given goal [49]. This system integrates a semantic cost function that considers sets of semantic places connected topologically through the robot’s actions or metric constraint.

From the approaches analysed, there is a consensus about the need for an object detection mechanism to provide a raise with the level of abstraction when representing concepts of the environment. Many works already include an object detection system [15,19,79,117,119,120] while others mention it as future works [111]. The analysis also offers other interesting insights such as the kind of elements of the environment considered to extract semantic knowledge. Some proposals integrate features of the environment [103,115,119,120], while other works use detected objects [15,19,79,119], and others analyse frames (stored real-world pictures and/or coordinates) [49,111,120]. Table 2 summarises the methods for place classification reviewed.

Interaction with users is also an important feature for a semantic navigator with many works integrating these kinds of systems [15,19,103,117]. Usually, these systems are based on spoken dialogues, except for Beeson et al., where a graphical interface is used instead of [103]. Finally, another common ground is the ability of a semantic navigator for learning new semantic knowledge as some works point out [15,19,79].

5. Open Issues and Questions

An extensive review of literature in semantic information for robot navigation raises some interesting questions that practitioners should take into consideration.

Is a knowledge representation model better than another? When comparing between knowledge representation models it is important to establish comparison criteria. This is still an open problem since abstract representations of information are not easy to evaluate. Experts in psychology have not reached a consensus about the knowledge representation that our brain uses to process information. In fact, multiple analogies have been used to try to understand our internal representation.

How essential is an object recognition mechanism in navigation? In general, proposals for semantic navigation include a object detection mechanisms. Most of the semantic navigators need information about the objects in the scene to provide high-level classification of places. Therefore, an object recognition mechanism is a key feature to integrate into such systems and it is desirable having a system as generic as possible, that is, without the need to be trained to detect all specific objects in the environment. For instance, a system should be able to recognize a new instance of an object (e.g., a chair) in any environment without specific training for that chair. New machine learning techniques are leading the way to achieve a high level of generalization, but at the cost of great amounts of data for training [121,122].

Are there environments more suitable for semantic navigation than others? This question has been discussed by many researchers. Despite its advantages, it is not clear yet how robots that use current semantic-based techniques for navigation will coexist with humans. One of the concerns regards the number of people in the environment as robots using high-level semantic navigation rely on object detection. A crowded environment is more prone to multiple occlusions, thus reducing the chances for information acquisition. Some authors propose solutions to this problem, but still extensive experiments are necessary to define the kind of environments more suitable for this type of navigation techniques.

Indoor semantic navigation methods are reusable in outdoor environments? This issue is usually arises when a new semantic navigation system emerges. In the state of the art, there are semantic navigators for indoor and also for outdoor environments, but are they so different? It is reasonable to consider whether the progress in either application is usable on the another. The question that remains open is whether a navigator will emerge that serves both for indoors and outdoors, having defined ontologies wide enough to consider the two levels of relationships and the functional sensory ability for both situations.

What is still lacking to see robots in everyday environments such as homes, hospitals, etc.? Semantic navigation involves several disciplines of robotics. For this reason, aspects such as object recognition and human-robot interaction must be integrated with a robust low-level navigation system.

What applications, other than navigation, can benefit from the same semantic map scheme? The management of semantic knowledge and the management of ontologies can be applied to other tasks apart from navigation. The RoboEarth project includes a knowledge system implemented with Prolog that allows a robot to accomplish tasks like cooking a pizza. Therefore, it is reasonable to think that the inclusion of semantic maps can be useful to develop other tasks. It can also be applied to user interaction to obtain information of any kind, not only relative to the navigation. Besides, object detection can be improved with the inclusion of the semantic map. This means that many applications can be improved by integrating techniques described in this work.

6. Conclusions

This contribution reviews the state of the art in mobile robot navigation paying special attention to the role of semantic information. From this idea, a high-level navigation category arises, that is, semantic navigation that can be defined as the ability of a robot to plan the path to its destination taking advantage of the high-level information provided by the elements of the environment. Exploiting this information in the real world allows more optimal navigation.

To achieve high-level navigation, some previous steps must be considered. First, information acquisition allows identifying the main components in the environment following two procedures. This process can be eased by interacting with humans, who will provide the information pieces required or, conversely, the robot can autonomously detect people, objects and other elements in the environment.

This ability to acquire information is an open problem that has been addressed in the contributions mentioned in the present work. In the case of autonomous detection, the elements found in the environment will be limited by the sensors the robot includes. There is a difference, therefore, between acquiring information directly from the environment and acquiring knowledge from interaction with humans since this last option allows more abstract relationships. The first case deals with the idea of recognizing objects, shapes, corners, or any item suitable for obtaining information from the environment. In this case, multiple techniques for object detection, segmentation and frame analysis have been included. In the second case, it deals with the idea of the system being able to learn new concepts, such as relationships. The most widespread technique among the authors facing this problem is the robot’s dialogue with a human user. Through this dialogue or interaction with the user, the robot learns new concepts. Interaction with humans is, therefore, a useful ability from two points of view. On the one hand, through the interaction the system recognizes the objective of the navigation. On the other hand, using interaction, the system can learn some concepts. From the works presented in this paper, it can be stated that interaction by voice and dialogue predominates. However, some authors choose a display or keyboard interface.

In this paper, different types of maps have also been reviewed. These maps can be used in semantic navigation. Two types of maps can be distinguished according to what different authors have been discussing, that is, semantic and cognitive maps. All authors agree that the way of mapping the environment define the navigation to be carried out. As more general conclusions, it is observed that the bio-inspired models are important. An attractive feature for semantic navigation is that it works with abstractions similar to those performed by humans to plan their path and classify their environment according to their usefulness.

The ability to reason is one of the main capabilities of a high-level navigator. The more concepts it can manage, the more knowledge it can extract. This allows that even when there is very little information, non-specific information or very abstract information, the robot reaches its destination. The works that have included concepts of high-level of abstraction have used ontologies that allow them to manage conceptual hierarchies. In this case, some reasoning system is added. These systems extract information and make inferences about the defined ontologies. The systems reviewed include a series of reasoning techniques, such as behaviour trees, finite state machines, reasoners based on descriptive logics and reasoners that access relational databases. These reasoners are identified as flexible and powerful, while those that access relational databases are faster and can work with a larger volume of concepts.

Regarding the components that a semantic navigation approach has to implement, different authors identified some common grounds. These are the low-level navigator, a high-level navigator and an interface that links both. The high-level navigator must allow working with a level of abstraction enough to represent semantic concepts of the environment to recognize and classify places. These minimum components are complex enough to generate different research directions within the semantic navigation. In this paper, works focused on one or more of the capabilities of the high-level navigator have been gathered.

From a technical point of view, it has been discussed that, although many works deal with some issue related to semantic navigation, there are not so many complete semantic navigators. However, some works provide interesting solutions to many of the problems separately. It can be expected that in the short or medium term, systems will emerge from the idea of integrating the different partial solutions.

For all this, it is foreseen that semantic navigation will improve in the next years, incorporating better systems for objects detection, as well as more general systems of reasoning and interacting with the user.

This survey required thorough bibliographical research in well-known databases. We used mainly Scopus, Web of Science (WoS), and Google Scholar. About the topics surveyed, we tried to cover the main aspects relative to semantic navigation, such as mapping, information acquisition, human-robot interaction, inference and reasoning, etc. Therefore, these were the main keywords derived from these topics. For the papers gathered in this survey, we considered those contributions that addressed one or more aspects related to a semantic navigation system. Additionally, some papers were included to explain concepts related to those fields (e.g., computer vision algorithms that were later integrated into a semantic navigation system).