Facing the realistic demands of the application environment of robots, the application of simultaneous localisation and mapping (SLAM) has gradually moved from static environments to complex dynamic environments, while traditional SLAM methods usuall...
In this work, a system is developed for semantic labeling of locations based on what people do. This system is useful for semantic navigation of mobile robots. The system differentiates environments according to what people do in them. Background sou...
Unmanned Aerial Vehicles (UAVs) require the ability to robustly perceive surrounding scenes for autonomous navigation. The semantic reconstruction of the scene is a truly functional understanding of the environment. However, high-performance computin...
To address the challenges of dynamic object interference and redundant information representation in map construction for indoor dynamic environments, this paper proposes an indoor dynamic environment mapping method based on semantic fusion and hiera...
Humans have an innate ability of environment modeling, perception, and planning while simultaneously performing tasks. However, it is still a challenging problem in the study of robotic cognition. We address this issue by proposing a neuro-inspired c...
Autonomous unmanned aircraft need a good semantic understanding of their surroundings to plan safe routes or to find safe landing sites, for example, by means of a semantic segmentation of an image stream. Currently, Neural Networks often give state-...
Semantic segmentation of 3D point clouds in drivable areas is very important for unmanned vehicles. Due to the imbalance between the size of various outdoor scene objects and the sample size, the object boundaries are not clear, and small sample feat...
LiDAR-based semantic segmentation, particularly for unstructured environments, plays a crucial role in environment perception and driving decisions for unmanned ground vehicles. Unfortunately, chaotic unstructured environments, especially the high-pr...
Traditional Simultaneous Localization and Mapping (SLAM) (with loop closure detection), or Visual Odometry (VO) (without loop closure detection), are based on the static environment assumption. When working in dynamic environments, they perform poorl...
When a traditional visual SLAM system works in a dynamic environment, it will be disturbed by dynamic objects and perform poorly. In order to overcome the interference of dynamic objects, we propose a semantic SLAM system for catadioptric panoramic c...
For autonomous vehicles driving in off-road environments, it is crucial to have a sensitive environmental perception ability. However, semantic segmentation in complex scenes remains a challenging task. Most current methods for off-road environments...
Global navigation satellite systems (GNSSs) exhibit significant performance limitations in signal-deprived environments such as indoor spaces and underground spaces. Although visual SLAM has emerged as a viable solution for ego-motion estimation in G...
High-accurate and real-time localization is the fundamental and challenging task for autonomous driving in a dynamic traffic environment. This paper presents a coordinated positioning strategy that is composed of semantic information and probabilisti...
To be able to react adequately a smart environment must be aware of the context and its changes. Modeling the context allows applications to better understand it and to adapt to its changes. In order to do this an appropriate formal representation me...
In the near future, the communication between autonomous cars will produce a network of sensors that will allow us to know the state of the roads in real time. Lidar technology, upon which most autonomous cars are based, allows the acquisition of 3D...
Accurately localizing individual trees and identifying species distribution are critical tasks in forestry remote sensing. Visual Simultaneous Localization and Mapping (visual SLAM) algorithms serve as important tools for outdoor spatial positioning...
Accurate obstacle detection plays a crucial role in the creation of high-precision maps within unstructured terrain environments, as it supplies vital decision-making information for unmanned engineering vehicles. Existing works primarily focus on th...
Simultaneous localization and mapping (SLAM) plays a crucial role in the field of intelligent mobile robots. However, the traditional Visual SLAM (VSLAM) framework is based on strong assumptions about static environments, which are not applicable to...
The increasing demand for cloud computing has shifted business toward a huge demand for cloud services, which offer platform, software, and infrastructure for the day-to-day use of cloud consumers. Numerous new cloud service providers have been intro...
In indoor low-light environments, the lack of light makes the captured images often suffer from quality degradation problems, including missing features in dark areas, noise interference, low brightness, and low contrast. Therefore, the feature extra...
The magnitude and multiformity of recorded and readily available urban data from independent sensors and mobile user devices have paved new possibilities for city environment planning and evolution. In previous works, a visual semantic decision suppo...
Health-related limitations prohibit a human from working in hazardous environments, due to which cognitive robots are needed to work there. A robot cannot learn the spatial semantics of the environment or object, which hinders the robot from interact...
Localization is one of the most important issues in mobile robotics, especially when an autonomous mobile robot performs a navigation task. The current and popular occupancy grid map, based on 2D LiDar simultaneous localization and mapping (SLAM), is...
Spectral confusion among land cover classes is quite common, let alone in a complex and heterogenous system like the semi-arid Mediterranean environment; thus, employing new developments in remote sensing, such as multispectral imagery (MSI) captured...
Aiming at the problem that dynamic targets in indoor environments lead to low accuracy and large errors in the localization and position estimation of visual SLAM systems and the inability to build maps containing semantic information, a semantic vis...
Recently, deep learning techniques, specifically semantic segmentation, have been employed to extract visual features from street images, a dimension that has received limited attention in the investigation of the connection between subjective and ob...
Recent developments in the area of cyber-physical systems (CPSs) and Internet of Things (IoT) are among the drivers for the emergence of the Industry 4.0 concept, setting new requirements for the architecture, technology, and design approaches of mod...
Perception of the indoor environment is the basis of mobile robot localization, navigation, and path planning, and it is particularly important to construct semantic maps in real time using minimal resources. The existing methods are too dependent on...
The low-altitude economy heavily relies on new carriers represented by unmanned aerial vehicles (UAVs). The localization accuracy of UAVs highly relies on the Global Navigation Satellite System (GNSS), which can be easily affected in low-altitude urb...
Visual simultaneous localization and mapping (VSLAM) enhances the navigation of autonomous agents in unfamiliar environments by progressively constructing maps and estimating poses. However, conventional VSLAM pipelines often exhibited degraded perfo...
Environment perception remains one of the key tasks in autonomous driving for which solutions have yet to reach maturity. Multi-modal approaches benefit from the complementary physical properties specific to each sensor technology used, boosting over...
Autonomous robot navigation has become a crucial concept in industrial development for minimizing manual tasks. Most of the existing robot navigation systems are based on the perceived geometrical features of the environment, with the employment of s...
Object detection is an essential task for autonomous vehicles to ensure safety. Due to the complexity of mining environments, it is difficult to detect objects accurately and robustly. To address these issues, this paper proposes a novel 3D LiDAR-bas...
Simultaneous Localization and Mapping (SLAM) remains challenging in dynamic environments. Recent approaches combining deep learning with algorithms for dynamic scenes comprise two types: faster, less accurate object detection-based methods and highly...
Significance and popularity of Role-Based Access Control (RBAC) is inevitable; however, its application is highly challenging in multi-domain collaborative smart city environments. The reason is its limitations in adapting the dynamically changing in...
The core of intelligent virtual geographical environments (VGEs) is the formal expression of geographic knowledge. Its purpose is to transform the data, information, and scenes of a virtual geographic environment into “knowledge” that can...
Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model. Recently, there has been an increasing amount of research related to i...
Natural heritage digitization has evolved beyond simple 3D representation. Contemporary approaches require transparent documentation integrating biological, heritage, and digitization standards, yet existing frameworks operate in isolated domains wit...
Although numerous effective Simultaneous Localization and Mapping (SLAM) systems have been developed, complex dynamic environments continue to present challenges, such as managing moving objects and enabling robots to comprehend environments. This pa...
Room usage semantics in models of large indoor environments such as public buildings and business complex are critical in many practical applications, such as health and safety regulations, compliance, and emergency response. Existing models such as...
The ecological environment is the basis of human survival and development. Effective methods to monitor the ecological environment are essential for the healthy development of human settlements. At present, methods based on remote sensing images and...
Three-dimensional semantic segmentation is the foundation for automatically creating enriched Digital Twin Cities (DTCs) and their updates. For this task, prior-level fusion approaches show more promising results than other fusion levels. This articl...
Visual Simultaneous Localization and Mapping (V-SLAM) plays a crucial role in the development of intelligent robotics and autonomous navigation systems. However, it still faces significant challenges in handling highly dynamic environments. The preva...
Indoor service robots need to build an object-centric semantic map to understand and execute human instructions. Conventional visual simultaneous localization and mapping (SLAM) systems build a map using geometric features such as points, lines, and...
Simultaneous Localization and Mapping (SLAM) is a fundamental technique in mobile robotics, enabling autonomous navigation and environmental reconstruction. However, dynamic elements in real-world scenes—such as walking pedestrians, moving vehi...
The evolution of ambient intelligence has introduced a range of new opportunities to improve people’s well-being. One of these opportunities is the use of these technologies to enhance workplaces and improve employees’ comfort and product...
The recognition of terrain and outdoor complex environments based on vision sensors is a key technology in practical robotics applications, and forms the basis of autonomous navigation and motion planning. While traditional machine learning methods c...
Common visual simultaneous localization and mapping systems are built on the static environment hypothesis and fail to handle the substantial environmental dynamics. Particularly in highly dynamic environments, the pose estimation errors tend to accu...
Omnidirectional and 360° images are becoming widespread in industry and in consumer society, causing omnidirectional computer vision to gain attention. Their wide field of view allows the gathering of a great amount of information about the envir...
Autonomous Underwater Vehicles (AUVs) play a critical role in ocean exploration. However, due to the inherent limitations of most sensors in underwater environments, achieving accurate navigation and localization in complex underwater scenarios remai...
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