SLAMSubTRobotic SystemsLidarAutonomy PackageAutonomous navigation of robots in harsh and GPS denied subterranean (SubT) environments with lack of natural or poor illumination is a challenging task that fosters the development of algorithms for pose estimation and mapping. Insp...
Object-wise comparison of LiDAR occupancy grid scan rendering methods Robotics and Autonomous Systems, Volume 161, 2023, Article 104363 Víctor Jiménez,…, Jorge Villagra View PDF Rendering the Directional TSDF for Tracking and Multi-Sensor Registration with Point-To-Plane Scale ICP Robotics and Auto...
LIDARALGORITHMSTRAFFIC safetyAUTONOMOUS vehiclesSafe autonomous driving is the future trend, and achieving it requires precise and real-time simultaneous localization and mapping (SLAM). Many practitioners are concerned about the performance of LiDAR SLAM algorithms, but there is little research work to ...
LIDARALGORITHMSNAVIGATIONSimultaneous localization and mapping (SLAM) is an area of research that is experiencing rapid advancements, with a significant impact on improving the navigation and perception capabilities of vehicles, thereby enabling their safe operation in complex environment...
Based on the assumption of functional capability of existing SLAM implementations, the paper evaluates specific details of urban car drives that arise when SLAM is to be used for automatic car control. In the presented case, LiDAR-based positioning is done with the Google Cartographer software ...
3D reconstructionroboticsSLAM (Simultaneous Localization and Mapping), primarily relying on camera or LiDAR (Light Detection and Ranging) sensors, plays a crucial role in robotics for localization and environmental reconstruction. This paper assesses the performance of two leading methods, namely ORB-SLAM...
This evaluation revealed that Visual SLAM requires more CPU resources than LiDAR SLAM, primarily due to additional data storage requirements, emphasizing the impact of environmental factors on resource requirements. In conclusion, LiDAR SLAM is more suitable for the outdoors due to its c...
In this paper, the performances of three representative 2D LiDAR SLAM algorithms, namely, Hector, GMapping, and Cartographer, in semi-structured orchard environments simulated in Gazebo are analysed. A hierarchical terrain modelling method is proposed to generate scalable orchard terrain with adjustable ...
This paper presents an experimental evaluation and comparison of LiDAR SLAM algorithms for mobile robotics. We analyze the performance of four state-of-the-art methods for localization and mapping in terms of the capability in reconstructing a point cloud of the surveyed environment and of the ...
Therefore, this study employed laser scanning to construct a point-surface model, which was based on a point cloud algorithm and SLAM (Simultaneous Localization and Mapping), integrated LiDAR and inertial navigation system data, and optimized the point model after processing steps such as denoising,...