Special Issue "Mobile Mapping Technologies"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 August 2019).

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Pablo Rodríguez-Gonzálvez
E-Mail
Guest Editor
Department of Mining Technology, Topography and Structures, University of León, Avda. Astorga, s/n, 24401 Ponferrada, Spain
Interests: photogrammetry; drones; laser scanning; radiometric calibration; remote sensing; rgb-d sensors; 3d modeling; mobile mapping
Special Issues and Collections in MDPI journals
Dr. Erica Nocerino
E-Mail Website
Guest Editor
1. LIS laboratory - Laboratoire d'informatique et Systèmes, I&M Team - Images & Models, Aix-Marseille Université, CNRS, ENSAM, Université De Toulon, Polytech, Luminy, Bat. A, case 925, 163 avenue de Luminy, 13288 Marseille cedex 9, France
2. Institute of Theoretical Physics, ETH Zurich, HIT G31.7, Wolfgang-Pauli-Strasse 278093 Zurich, Switzerland
Interests: Photogrammetry; underwater; calibration; image processing; laser scanning; 3D modelling
Special Issues and Collections in MDPI journals
Dr. Isabella Toschi
E-Mail Website
Guest Editor
Bruno Kessler Foundation, Via Santa Croce, 77, 38122 Trento, Italy
Interests: Photogrammetry; Laser scanning; Airborne mapping; 3D modelling; Calibration; Mobile mapping systems
Special Issues and Collections in MDPI journals
Prof. Kai-Wei Chiang
E-Mail Website
Guest Editor
Department of Geomatics, National Cheng Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan
Interests: inertial navigation system; optimal multi-sensor fusion; seamless mapping and navigation applications; artificial Intelligence and collaborative mobile mapping technology
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Mobile mapping technologies have seen a rapid growth of research activity and interest in the last years, due to the increased demand for accurate, dense and geo-referenced 3D data. Their main characteristic is the ability to acquire 3D information for large areas dynamically. This versatility has expanded their application fields from civil engineering to a broad range of fields (industry, emergency response, cultural heritage...), which is still widening. This increased number of needs, some of them especially challenging, is pushing the scientific community, as well as companies, to propose innovative solutions, ranging from new hardware/open source software approaches and integration with other devices, to the adoption and development of artificial intelligence methods for the automatic extraction of salient features and quality assessments for performance verification

The aim of the present Special Issue is to cover the relevant topics and trends in mobile mapping technology, and also to introduce the new tendencies of this new paradigm in geospatial science.

We would like to invite you to contribute by submitting articles describing your recent research, experimental work, reviews and/or case studies related to the field of mobile mapping. Contributions may be from, but not limited to, the following topics:

  • Sensor design and platform developments.
  • Geometric calibration.
  • Radiometry: calibration and texturing.
  • Multi-source data fusion.
  • Hybridization with other data sources.
  • Simultaneous localization and mapping.
  • Low-cost solutions.
  • Portable mobile mapping systems.
  • Point cloud processing.
  • Feature extraction.
  • Machine learning.
  • Deep learning.
  • Accuracy, precision and quality assessment.
  • Verification and validation.
  • Indoor mapping and navigation.
  • Autonomous navigation.
  • GNSS-denied environments.
  • Urban analysis.
  • Forest mapping.
  • Post-disaster assessment.
  • Novel application cases.

Dr. Pablo Rodríguez-Gonzálvez
Dr. Erica Nocerino
Dr. Isabella Toschi
Prof. Dr. Kai-Wei Chiang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Mobile laser scanning
  • Portable mobile mapping systems
  • Calibration
  • Airborne laser scanning
  • Data fusion
  • Sensor integration
  • Feature extraction
  • 3D modelling
  • Georeferencing
  • Verification and validation
  • Accuracy and precision assessment

Published Papers (14 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Other

Open AccessArticle
A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction
Remote Sens. 2019, 11(21), 2572; https://doi.org/10.3390/rs11212572 - 01 Nov 2019
Abstract
High-accuracy indoor positioning is a prerequisite to satisfy the increasing demands of position-based services in complex indoor scenes. Current indoor visual-positioning methods mainly include image retrieval-based methods, visual landmarks-based methods, and learning-based methods. To better overcome the limitations of traditional methods such as [...] Read more.
High-accuracy indoor positioning is a prerequisite to satisfy the increasing demands of position-based services in complex indoor scenes. Current indoor visual-positioning methods mainly include image retrieval-based methods, visual landmarks-based methods, and learning-based methods. To better overcome the limitations of traditional methods such as them being labor-intensive, of poor accuracy, and time-consuming, this paper proposes a novel indoor-positioning method with automated red, green, blue and depth (RGB-D) image database construction. First, strategies for automated database construction are developed to reduce the workload of manually selecting database images and ensure the requirements of high-accuracy indoor positioning. The database is automatically constructed according to the rules, which is more objective and improves the efficiency of the image-retrieval process. Second, by combining the automated database construction module, convolutional neural network (CNN)-based image-retrieval module, and strict geometric relations-based pose estimation module, we obtain a high-accuracy indoor-positioning system. Furthermore, in order to verify the proposed method, we conducted extensive experiments on the public indoor environment dataset. The detailed experimental results demonstrated the effectiveness and efficiency of our indoor-positioning method. Full article
(This article belongs to the Special Issue Mobile Mapping Technologies) Printed Edition available
Show Figures

Graphical abstract

Open AccessArticle
Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems
Remote Sens. 2019, 11(20), 2365; https://doi.org/10.3390/rs11202365 - 12 Oct 2019
Abstract
The 3D digital characterization of vegetation is a growing practice in the agronomy sector. Precision agriculture is sustained, among other methods, by variables that remote sensing techniques can digitize. At present, laser scanners make it possible to digitize three-dimensional crop geometry in the [...] Read more.
The 3D digital characterization of vegetation is a growing practice in the agronomy sector. Precision agriculture is sustained, among other methods, by variables that remote sensing techniques can digitize. At present, laser scanners make it possible to digitize three-dimensional crop geometry in the form of point clouds. In this work, we developed several methods for calculating the volume of vine wood, with the final intention of using these values as indicators of vegetative vigor on a thematic map. For this, we used a static terrestrial laser scanner (TLS), a mobile scanning system (MMS), and six algorithms that were implemented and adapted to the data captured and to the proposed objective. The results show that, with TLS equipment and the algorithm called convex hull cluster, the volumes of a vine trunk can be obtained with a relative error lower than 7%. Although the accuracy and detail of the cloud obtained with TLS are very high, the cost per unit for the scanned area limits the application of this system for large areas. In contrast to the inoperability of the TLS in large areas of terrain, the MMS and the algorithm based on the L1-medial skeleton and the modelling of cylinders of a certain height and diameter have solved the estimation of volumes with a relative error better than 3%. To conclude, the vigor map elaborated represents the estimated volume of each vine by this method. Full article
(This article belongs to the Special Issue Mobile Mapping Technologies) Printed Edition available
Show Figures

Graphical abstract

Open AccessFeature PaperArticle
Validation of Portable Mobile Mapping System for Inspection Tasks in Thermal and Fluid–Mechanical Facilities
Remote Sens. 2019, 11(19), 2205; https://doi.org/10.3390/rs11192205 - 20 Sep 2019
Abstract
The three-dimensional registration of industrial facilities has a great importance for maintenance, inspection, and safety tasks and it is a starting point for new improvements and expansions in the industrial facilities context. In this paper, a comparison between the results obtained using a [...] Read more.
The three-dimensional registration of industrial facilities has a great importance for maintenance, inspection, and safety tasks and it is a starting point for new improvements and expansions in the industrial facilities context. In this paper, a comparison between the results obtained using a novel portable mobile mapping system (PMMS) and a static terrestrial laser scanner (TLS), widely used for 3D reconstruction in civil and industrial scenarios, is carried out. This comparison is performed in the context of industrial inspection tasks, specifically in the thermal and fluid-mechanics facilities in a hospital. The comparison addresses the general reconstruction of a machine room, focusing on the quantitative and qualitative analysis of different elements (e.g., valves, regulation systems, burner systems and tanks, etc.). The validation of the PMMS is provided considering the TLS as ground truth and applying a robust statistical analysis. Results come to confirm the suitability of the PMMS to perform inspection tasks in industrial facilities. Full article
(This article belongs to the Special Issue Mobile Mapping Technologies) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
Forward and Backward Visual Fusion Approach to Motion Estimation with High Robustness and Low Cost
Remote Sens. 2019, 11(18), 2139; https://doi.org/10.3390/rs11182139 - 13 Sep 2019
Abstract
We present a novel low-cost visual odometry method of estimating the ego-motion (self-motion) for ground vehicles by detecting the changes that motion induces on the images. Different from traditional localization methods that use differential global positioning system (GPS), precise inertial measurement unit (IMU) [...] Read more.
We present a novel low-cost visual odometry method of estimating the ego-motion (self-motion) for ground vehicles by detecting the changes that motion induces on the images. Different from traditional localization methods that use differential global positioning system (GPS), precise inertial measurement unit (IMU) or 3D Lidar, the proposed method only leverage data from inexpensive visual sensors of forward and backward onboard cameras. Starting with the spatial-temporal synchronization, the scale factor of backward monocular visual odometry was estimated based on the MSE optimization method in a sliding window. Then, in trajectory estimation, an improved two-layers Kalman filter was proposed including orientation fusion and position fusion. Where, in the orientation fusion step, we utilized the trajectory error space represented by unit quaternion as the state of the filter. The resulting system enables high-accuracy, low-cost ego-pose estimation, along with providing robustness capability of handing camera module degradation by automatic reduce the confidence of failed sensor in the fusion pipeline. Therefore, it can operate in the presence of complex and highly dynamic motion such as enter-in-and-out tunnel entrance, texture-less, illumination change environments, bumpy road and even one of the cameras fails. The experiments carried out in this paper have proved that our algorithm can achieve the best performance on evaluation indexes of average in distance (AED), average in X direction (AEX), average in Y direction (AEY), and root mean square error (RMSE) compared to other state-of-the-art algorithms, which indicates that the output results of our approach is superior to other methods. Full article
(This article belongs to the Special Issue Mobile Mapping Technologies) Printed Edition available
Show Figures

Graphical abstract

Open AccessArticle
Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features
Remote Sens. 2019, 11(16), 1955; https://doi.org/10.3390/rs11161955 - 20 Aug 2019
Abstract
Mobile Mapping is an efficient technology to acquire spatial data of the environment. The spatial data is fundamental for applications in crisis management, civil engineering or autonomous driving. The extrinsic calibration of the Mobile Mapping System is a decisive factor that affects the [...] Read more.
Mobile Mapping is an efficient technology to acquire spatial data of the environment. The spatial data is fundamental for applications in crisis management, civil engineering or autonomous driving. The extrinsic calibration of the Mobile Mapping System is a decisive factor that affects the quality of the spatial data. Many existing extrinsic calibration approaches require the use of artificial targets in a time-consuming calibration procedure. Moreover, they are usually designed for a specific combination of sensors and are, thus, not universally applicable. We introduce a novel extrinsic self-calibration algorithm, which is fully automatic and completely data-driven. The fundamental assumption of the self-calibration is that the calibration parameters are estimated the best when the derived point cloud represents the real physical circumstances the best. The cost function we use to evaluate this is based on geometric features which rely on the 3D structure tensor derived from the local neighborhood of each point. We compare different cost functions based on geometric features and a cost function based on the Rényi quadratic entropy to evaluate the suitability for the self-calibration. Furthermore, we perform tests of the self-calibration on synthetic and two different real datasets. The real datasets differ in terms of the environment, the scale and the utilized sensors. We show that the self-calibration is able to extrinsically calibrate Mobile Mapping Systems with different combinations of mapping and pose estimation sensors such as a 2D laser scanner to a Motion Capture System and a 3D laser scanner to a stereo camera and ORB-SLAM2. For the first dataset, the parameters estimated by our self-calibration lead to a more accurate point cloud than two comparative approaches. For the second dataset, which has been acquired via a vehicle-based mobile mapping, our self-calibration achieves comparable results to a manually refined reference calibration, while it is universally applicable and fully automated. Full article
(This article belongs to the Special Issue Mobile Mapping Technologies) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
An Accurate Visual-Inertial Integrated Geo-Tagging Method for Crowdsourcing-Based Indoor Localization
Remote Sens. 2019, 11(16), 1912; https://doi.org/10.3390/rs11161912 - 16 Aug 2019
Abstract
One of the unavoidable bottlenecks in the public application of passive signal (e.g., received signal strength, magnetic) fingerprinting-based indoor localization technologies is the extensive human effort that is required to construct and update database for indoor positioning. In this paper, we propose an [...] Read more.
One of the unavoidable bottlenecks in the public application of passive signal (e.g., received signal strength, magnetic) fingerprinting-based indoor localization technologies is the extensive human effort that is required to construct and update database for indoor positioning. In this paper, we propose an accurate visual-inertial integrated geo-tagging method that can be used to collect fingerprints and construct the radio map by exploiting the crowdsourced trajectory of smartphone users. By integrating multisource information from the smartphone sensors (e.g., camera, accelerometer, and gyroscope), this system can accurately reconstruct the geometry of trajectories. An algorithm is proposed to estimate the spatial location of trajectories in the reference coordinate system and construct the radio map and geo-tagged image database for indoor positioning. With the help of several initial reference points, this algorithm can be implemented in an unknown indoor environment without any prior knowledge of the floorplan or the initial location of crowdsourced trajectories. The experimental results show that the average calibration error of the fingerprints is 0.67 m. A weighted k-nearest neighbor method (without any optimization) and the image matching method are used to evaluate the performance of constructed multisource database. The average localization error of received signal strength (RSS) based indoor positioning and image based positioning are 3.2 m and 1.2 m, respectively, showing that the quality of the constructed indoor radio map is at the same level as those that were constructed by site surveying. Compared with the traditional site survey based positioning cost, this system can greatly reduce the human labor cost, with the least external information. Full article
(This article belongs to the Special Issue Mobile Mapping Technologies) Printed Edition available
Show Figures

Graphical abstract

Open AccessArticle
Feasibility of Using Grammars to Infer Room Semantics
Remote Sens. 2019, 11(13), 1535; https://doi.org/10.3390/rs11131535 - 28 Jun 2019
Abstract
Current indoor mapping approaches can detect accurate geometric information but are incapable of detecting the room type or dismiss this issue. This work investigates the feasibility of inferring the room type by using grammars based on geometric maps. Specifically, we take the research [...] Read more.
Current indoor mapping approaches can detect accurate geometric information but are incapable of detecting the room type or dismiss this issue. This work investigates the feasibility of inferring the room type by using grammars based on geometric maps. Specifically, we take the research buildings at universities as examples and create a constrained attribute grammar to represent the spatial distribution characteristics of different room types as well as the topological relations among them. Based on the grammar, we propose a bottom-up approach to construct a parse forest and to infer the room type. During this process, Bayesian inference method is used to calculate the initial probability of belonging an enclosed room to a certain type given its geometric properties (e.g., area, length, and width) that are extracted from the geometric map. The approach was tested on 15 maps with 408 rooms. In 84% of cases, room types were defined correctly. It, to a certain degree, proves that grammars can benefit semantic enrichment (in particular, room type tagging). Full article
(This article belongs to the Special Issue Mobile Mapping Technologies) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving
Remote Sens. 2019, 11(11), 1348; https://doi.org/10.3390/rs11111348 - 04 Jun 2019
Cited by 3
Abstract
Real-time and high-precision localization information is vital for many modules of unmanned vehicles. At present, a high-cost RTK (Real Time Kinematic) and IMU (Integrated Measurement Unit) integrated navigation system is often used, but its accuracy cannot meet the requirements and even fails in [...] Read more.
Real-time and high-precision localization information is vital for many modules of unmanned vehicles. At present, a high-cost RTK (Real Time Kinematic) and IMU (Integrated Measurement Unit) integrated navigation system is often used, but its accuracy cannot meet the requirements and even fails in many scenes. In order to reduce the costs and improve the localization accuracy and stability, we propose a precise and robust segmentation-based Lidar (Light Detection and Ranging) localization system aided with MEMS (Micro-Electro-Mechanical System) IMU and designed for high level autonomous driving. Firstly, we extracted features from the online frame using a series of proposed efficient low-level semantic segmentation-based multiple types feature extraction algorithms, including ground, road-curb, edge, and surface. Next, we matched the adjacent frames in Lidar odometry module and matched the current frame with the dynamically loaded pre-build feature point cloud map in Lidar localization module based on the extracted features to precisely estimate the 6DoF (Degree of Freedom) pose, through the proposed priori information considered category matching algorithm and multi-group-step L-M (Levenberg-Marquardt) optimization algorithm. Finally, the lidar localization results were fused with MEMS IMU data through a state-error Kalman filter to produce smoother and more accurate localization information at a high frequency of 200Hz. The proposed localization system can achieve 3~5 cm in position and 0.05~0.1° in orientation RMS (Root Mean Square) accuracy and outperform previous state-of-the-art systems. The robustness and adaptability have been verified with localization testing data more than 1000 Km in various challenging scenes, including congested urban roads, narrow tunnels, textureless highways, and rain-like harsh weather. Full article
(This article belongs to the Special Issue Mobile Mapping Technologies) Printed Edition available
Show Figures

Graphical abstract

Open AccessArticle
Design, Calibration, and Evaluation of a Backpack Indoor Mobile Mapping System
Remote Sens. 2019, 11(8), 905; https://doi.org/10.3390/rs11080905 - 13 Apr 2019
Cited by 5
Abstract
Indoor mobile mapping systems are important for a wide range of applications starting from disaster management to straightforward indoor navigation. This paper presents the design and performance of a low-cost backpack indoor mobile mapping system (ITC-IMMS) that utilizes a combination of laser range-finders [...] Read more.
Indoor mobile mapping systems are important for a wide range of applications starting from disaster management to straightforward indoor navigation. This paper presents the design and performance of a low-cost backpack indoor mobile mapping system (ITC-IMMS) that utilizes a combination of laser range-finders (LRFs) to fully recover the 3D building model based on a feature-based simultaneous localization and mapping (SLAM) algorithm. Specifically, we use robust planar features. These are advantageous, because oftentimes the final representation of the indoor environment is wanted in a planar form, and oftentimes the walls in an indoor environment physically have planar shapes. In order to understand the potential accuracy of our indoor models and to assess the system’s ability to capture the geometry of indoor environments, we develop novel evaluation techniques. In contrast to the state-of-the-art evaluation methods that rely on ground truth data, our evaluation methods can check the internal consistency of the reconstructed map in the absence of any ground truth data. Additionally, the external consistency can be verified with the often available as-planned state map of the building. The results demonstrate that our backpack system can capture the geometry of the test areas with angle errors typically below 1.5° and errors in wall thickness around 1 cm. An optimal configuration for the sensors is determined through a set of experiments that makes use of the developed evaluation techniques. Full article
(This article belongs to the Special Issue Mobile Mapping Technologies) Printed Edition available
Show Figures

Graphical abstract

Open AccessArticle
DRE-SLAM: Dynamic RGB-D Encoder SLAM for a Differential-Drive Robot
Remote Sens. 2019, 11(4), 380; https://doi.org/10.3390/rs11040380 - 13 Feb 2019
Cited by 2
Abstract
The state-of-the-art visual simultaneous localization and mapping (V-SLAM) systems have high accuracy localization capabilities and impressive mapping effects. However, most of these systems assume that the operating environment is static, thereby limiting their application in the real dynamic world. In this paper, by [...] Read more.
The state-of-the-art visual simultaneous localization and mapping (V-SLAM) systems have high accuracy localization capabilities and impressive mapping effects. However, most of these systems assume that the operating environment is static, thereby limiting their application in the real dynamic world. In this paper, by fusing the information of an RGB-D camera and two encoders that are mounted on a differential-drive robot, we aim to estimate the motion of the robot and construct a static background OctoMap in both dynamic and static environments. A tightly coupled feature-based method is proposed to fuse the two types of information based on the optimization. Dynamic pixels occupied by dynamic objects are detected and culled to cope with dynamic environments. The ability to identify the dynamic pixels on both predefined and undefined dynamic objects is available, which is attributed to the combination of the CPU-based object detection method and a multiview constraint-based approach. We first construct local sub-OctoMaps by using the keyframes and then fuse the sub-OctoMaps into a full OctoMap. This submap-based approach gives the OctoMap the ability to deform, and significantly reduces the map updating time and memory costs. We evaluated the proposed system in various dynamic and static scenes. The results show that our system possesses competitive pose accuracy and high robustness, as well as the ability to construct a clean static OctoMap in dynamic scenes. Full article
(This article belongs to the Special Issue Mobile Mapping Technologies) Printed Edition available
Show Figures

Graphical abstract

Open AccessArticle
A Flexible Architecture for Extracting Metro Tunnel Cross Sections from Terrestrial Laser Scanning Point Clouds
Remote Sens. 2019, 11(3), 297; https://doi.org/10.3390/rs11030297 - 01 Feb 2019
Cited by 1
Abstract
This paper presents a novel framework to extract metro tunnel cross sections (profiles) from Terrestrial Laser Scanning point clouds. The entire framework consists of two steps: tunnel central axis extraction and cross section determination. In tunnel central extraction, we propose a slice-based method [...] Read more.
This paper presents a novel framework to extract metro tunnel cross sections (profiles) from Terrestrial Laser Scanning point clouds. The entire framework consists of two steps: tunnel central axis extraction and cross section determination. In tunnel central extraction, we propose a slice-based method to obtain an initial central axis, which is further divided into linear and nonlinear circular segments by an enhanced Random Sample Consensus (RANSAC) tunnel axis segmentation algorithm. This algorithm transforms the problem of hybrid linear and nonlinear segment extraction into a sole segmentation of linear elements defined at the tangent space rather than raw data space, significantly simplifying the tunnel axis segmentation. The extracted axis segments are then provided as input to the step of the cross section determination which generates the coarse cross-sectional points by intersecting a series of straight lines that rotate orthogonally around the tunnel axis with their local fitted quadric surface, i.e., cylindrical surface. These generated profile points are further refined and densified via solving a constrained nonlinear least squares problem. Our experiments on Nanjing metro tunnel show that the cross sectional fitting error is only 1.69 mm. Compared with the designed radius of the metro tunnel, the RMSE (Root Mean Square Error) of extracted cross sections’ radii only keeps 1.60 mm. We also test our algorithm on another metro tunnel in Shanghai, and the results show that the RMSE of radii only keeps 4.60 mm which is superior to a state-of-the-art method of 6.00 mm. Apart from the accurate geometry, our approach can maintain the correct topology among cross sections, thereby guaranteeing the production of geometric tunnel model without crack defects. Moreover, we prove that our algorithm is insensitive to the missing data and point density. Full article
(This article belongs to the Special Issue Mobile Mapping Technologies) Printed Edition available
Show Figures

Graphical abstract

Open AccessArticle
Rapid Relocation Method for Mobile Robot Based on Improved ORB-SLAM2 Algorithm
Remote Sens. 2019, 11(2), 149; https://doi.org/10.3390/rs11020149 - 14 Jan 2019
Cited by 9
Abstract
In order to realize fast real-time positioning after a mobile robot starts, this paper proposes an improved ORB-SLAM2 algorithm. Firstly, we proposed a binary vocabulary storage method and vocabulary training algorithm based on an improved Oriented FAST and Rotated BRIEF (ORB) operator to [...] Read more.
In order to realize fast real-time positioning after a mobile robot starts, this paper proposes an improved ORB-SLAM2 algorithm. Firstly, we proposed a binary vocabulary storage method and vocabulary training algorithm based on an improved Oriented FAST and Rotated BRIEF (ORB) operator to reduce the vocabulary size and improve the loading speed of the vocabulary and tracking accuracy. Secondly, we proposed an offline map construction algorithm based on the map element and keyframe database; then, we designed a fast reposition method of the mobile robot based on the offline map. Finally, we presented an offline visualization method for map elements and mapping trajectories. In order to check the performance of the algorithm in this paper, we built a mobile robot platform based on the EAI-B1 mobile chassis, and we implemented the rapid relocation method of the mobile robot based on improved ORB SLAM2 algorithm by using C++ programming language. The experimental results showed that the improved ORB SLAM2 system outperforms the original system regarding start-up speed, tracking and positioning accuracy, and human–computer interaction. The improved system was able to build and load offline maps, as well as perform rapid relocation and global positioning tracking. In addition, our experiment also shows that the improved system is robust against a dynamic environment. Full article
(This article belongs to the Special Issue Mobile Mapping Technologies) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
Indoor Topological Localization Using a Visual Landmark Sequence
Remote Sens. 2019, 11(1), 73; https://doi.org/10.3390/rs11010073 - 03 Jan 2019
Cited by 3
Abstract
This paper presents a novel indoor topological localization method based on mobile phone videos. Conventional methods suffer from indoor dynamic environmental changes and scene ambiguity. The proposed Visual Landmark Sequence-based Indoor Localization (VLSIL) method is capable of addressing problems by taking steady indoor [...] Read more.
This paper presents a novel indoor topological localization method based on mobile phone videos. Conventional methods suffer from indoor dynamic environmental changes and scene ambiguity. The proposed Visual Landmark Sequence-based Indoor Localization (VLSIL) method is capable of addressing problems by taking steady indoor objects as landmarks. Unlike many feature or appearance matching-based localization methods, our method utilizes highly abstracted landmark sematic information to represent locations and thus is invariant to illumination changes, temporal variations, and occlusions. We match consistently detected landmarks against the topological map based on the occurrence order in the videos. The proposed approach contains two components: a convolutional neural network (CNN)-based landmark detector and a topological matching algorithm. The proposed detector is capable of reliably and accurately detecting landmarks. The other part is the matching algorithm built on the second order hidden Markov model and it can successfully handle the environmental ambiguity by fusing sematic and connectivity information of landmarks. To evaluate the method, we conduct extensive experiments on the real world dataset collected in two indoor environments, and the results show that our deep neural network-based indoor landmark detector accurately detects all landmarks and is expected to be utilized in similar environments without retraining and that VLSIL can effectively localize indoor landmarks. Full article
(This article belongs to the Special Issue Mobile Mapping Technologies) Printed Edition available
Show Figures

Figure 1

Other

Jump to: Research

Open AccessTechnical Note
Use of a Wearable Mobile Laser System in Seamless Indoor 3D Mapping of a Complex Historical Site
Remote Sens. 2018, 10(12), 1897; https://doi.org/10.3390/rs10121897 - 28 Nov 2018
Cited by 5
Abstract
This paper presents an efficient solution, based on a wearable mobile laser system (WMLS), for the digitalization and modelling of a complex cultural heritage building. A procedural pipeline is formalized for the data acquisition, processing and generation of cartographic products over a XV [...] Read more.
This paper presents an efficient solution, based on a wearable mobile laser system (WMLS), for the digitalization and modelling of a complex cultural heritage building. A procedural pipeline is formalized for the data acquisition, processing and generation of cartographic products over a XV century palace located in Segovia, Spain. The complexity, represented by an intricate interior space and by the presence of important structural problems, prevents the use of standard protocols such as those based on terrestrial photogrammetry or terrestrial laser scanning, making the WMLS the most suitable and powerful solution for the design of restoration actions. The results obtained corroborate with the robustness and accuracy of the digitalization strategy, allowing for the generation of 3D models and 2D cartographic products with the required level of quality and time needed to digitalize the area by a terrestrial laser scanner. Full article
(This article belongs to the Special Issue Mobile Mapping Technologies) Printed Edition available
Show Figures

Graphical abstract

Back to TopTop