In this paper,we present a distributed framework for the lidar-based relative state estimator which achieves highly accurate,real-time trajectory estimation of multiple Unmanned Aerial Vehicles(UAVs)in GPS-denied envi...In this paper,we present a distributed framework for the lidar-based relative state estimator which achieves highly accurate,real-time trajectory estimation of multiple Unmanned Aerial Vehicles(UAVs)in GPS-denied environments.The system builds atop a factor graph,and only on-board sensors and computing power are utilized.Benefiting from the keyframe strategy,each UAV performs relative state estimation individually and broadcasts very partial information without exchanging raw data.The complete system runs in real-time and is evaluated with three experiments in different environments.Experimental results show that the proposed distributed approach offers comparable performance with a centralized method in terms of accuracy and real-time performance.The flight test demonstrates that the proposed relative state estimation framework is able to be used for aggressive flights over 5 m/s.展开更多
In recent years,significant advancements have been made in enabling intelligent unmanned agents to achieve autonomous navigation and positioning within large-scale indoor or underground environments.Central to these a...In recent years,significant advancements have been made in enabling intelligent unmanned agents to achieve autonomous navigation and positioning within large-scale indoor or underground environments.Central to these achievements is simultaneous localization and mapping(SLAM)technology.Concurrently,the rapid evolution of LiDAR technologies has revolutionised SLAM,enhancing localisation and mapping capabilities in extreme environments characterised by high dynamics,sparse features or GPS-denied environment.Although much research has concentrated on camera-based SLAM or GPS-fused SLAM,this paper provides a comprehensive review of the development of LiDAR-based multi-sensor fusion SLAM with a particular emphasis on GPS-denied environments and filter-based sensor fusion techniques.The paper is structured as follows:The first section introduces the relevant hardware and datasets.The second section delves into the localisation methodologies employed.The third section discusses the mapping processes involved.The fourth section addresses open problems and suggests future research directions.Overall,this review aims to offer a thorough analysis of the development trends in SLAM with a focus on LiDAR-based methods,covering both hardware and software aspects,providing readers with a clear reference on workflow for engineering deliverable technologies that can be adapted to various application scenarios.展开更多
Improving the accuracy of digital elevation is essential for reducing hydro-topographic derivation errors pertaining to, e.g., flow direction, basin borders, channel networks, depressions, flood forecasting, and soil ...Improving the accuracy of digital elevation is essential for reducing hydro-topographic derivation errors pertaining to, e.g., flow direction, basin borders, channel networks, depressions, flood forecasting, and soil drainage. This article demonstrates how a gain in this accuracy is improved through digital elevation model (DEM) fusion, and using LiDAR-derived elevation layers for conformance testing and validation. This demonstration is done for the Province of New Brunswick (NB, Canada), using five province-wide DEM sources (SRTM 90 m;SRTM 30 m;ASTER 30 m;CDED 22 m;NB-DEM 10 m) and a five-stage process that guides the re-projection of these DEMs while minimizing their elevational differences relative to LiDAR-captured bare-earth DEMs, through calibration and validation. This effort decreased the resulting non-LiDAR to LiDAR elevation differences by a factor of two, reduced the minimum distance conformance between the non-LiDAR and LiDAR-derived flow channels to ± 10 m at 8.5 times out of 10, and dropped the non-LiDAR wet-area percentages of false positives from 59% to 49%, and of false negatives from 14% to 7%. While these reductions are modest, they are nevertheless not only consistent with already existing hydrographic data layers informing about stream and wet-area locations, they also extend these data layers across the province by comprehensively locating previously unmapped flow channels and wet areas.展开更多
基金supported by the National Key Research and Development Program of China(No.2018AAA0102401)the National Natural Science Foundation of China(Nos.62022060,61773278,61873340).
文摘In this paper,we present a distributed framework for the lidar-based relative state estimator which achieves highly accurate,real-time trajectory estimation of multiple Unmanned Aerial Vehicles(UAVs)in GPS-denied environments.The system builds atop a factor graph,and only on-board sensors and computing power are utilized.Benefiting from the keyframe strategy,each UAV performs relative state estimation individually and broadcasts very partial information without exchanging raw data.The complete system runs in real-time and is evaluated with three experiments in different environments.Experimental results show that the proposed distributed approach offers comparable performance with a centralized method in terms of accuracy and real-time performance.The flight test demonstrates that the proposed relative state estimation framework is able to be used for aggressive flights over 5 m/s.
基金supported by the research projects titled‘3D tunnel map reconstruction for mining ground vehicles’(Grant RDS10120240278)‘Drone-assisted forest patrol’(Grant RDS10120250081)+1 种基金‘Development of drone hangar embedded with city light poles’(Grant RDS10120250067)support provided by these projects has laid a solid foundation for the data collection,experimental verification and result analysis of this research,making the smooth progress of the study possible.
文摘In recent years,significant advancements have been made in enabling intelligent unmanned agents to achieve autonomous navigation and positioning within large-scale indoor or underground environments.Central to these achievements is simultaneous localization and mapping(SLAM)technology.Concurrently,the rapid evolution of LiDAR technologies has revolutionised SLAM,enhancing localisation and mapping capabilities in extreme environments characterised by high dynamics,sparse features or GPS-denied environment.Although much research has concentrated on camera-based SLAM or GPS-fused SLAM,this paper provides a comprehensive review of the development of LiDAR-based multi-sensor fusion SLAM with a particular emphasis on GPS-denied environments and filter-based sensor fusion techniques.The paper is structured as follows:The first section introduces the relevant hardware and datasets.The second section delves into the localisation methodologies employed.The third section discusses the mapping processes involved.The fourth section addresses open problems and suggests future research directions.Overall,this review aims to offer a thorough analysis of the development trends in SLAM with a focus on LiDAR-based methods,covering both hardware and software aspects,providing readers with a clear reference on workflow for engineering deliverable technologies that can be adapted to various application scenarios.
文摘Improving the accuracy of digital elevation is essential for reducing hydro-topographic derivation errors pertaining to, e.g., flow direction, basin borders, channel networks, depressions, flood forecasting, and soil drainage. This article demonstrates how a gain in this accuracy is improved through digital elevation model (DEM) fusion, and using LiDAR-derived elevation layers for conformance testing and validation. This demonstration is done for the Province of New Brunswick (NB, Canada), using five province-wide DEM sources (SRTM 90 m;SRTM 30 m;ASTER 30 m;CDED 22 m;NB-DEM 10 m) and a five-stage process that guides the re-projection of these DEMs while minimizing their elevational differences relative to LiDAR-captured bare-earth DEMs, through calibration and validation. This effort decreased the resulting non-LiDAR to LiDAR elevation differences by a factor of two, reduced the minimum distance conformance between the non-LiDAR and LiDAR-derived flow channels to ± 10 m at 8.5 times out of 10, and dropped the non-LiDAR wet-area percentages of false positives from 59% to 49%, and of false negatives from 14% to 7%. While these reductions are modest, they are nevertheless not only consistent with already existing hydrographic data layers informing about stream and wet-area locations, they also extend these data layers across the province by comprehensively locating previously unmapped flow channels and wet areas.
