![]() ![]() LeGO-LOAM: Lightweight andground-optimized LiDAR odometry and mapping on variable terrain//Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). ![]() Visual-LiDAR odometry and mapping: low-drift, robust, and fast//Proceedings of 2015 IEEE International Conference on Robotics and Automation (ICRA). LOAM: LiDAR odometry and mapping in real-time//Proceedings of 2014 Robotics: Science and Systems Conference (RSS). DI Kaichang, WAN Wenhui, ZHAO Hongying, et al. A review of visual-inertial simultaneous localization and mapping from filtering-based and optimization-based perspectives. IEEE Transactions on Instrumentation and Measurement, 2020, 70: 1-9. An integrated GNSS/LiDAR-SLAM pose estimation framework for large-scale map building in partially GNSS-denied environments. High definition 3D map creation using GNSS/IMU/LiDAR sensor integration to support autonomous vehicle navigation. High definition map for automated driving: overview and analysis. A review of visual-LiDAR fusion based simultaneous localization and mapping. Urban 3D modeling with mobile laser scanning: a review. Progress, challenges and perspectives of 3D LiDAR point cloud processing. YANG Bisheng, LIANG Fuxun, HUANG Ronggang. ![]() A survey of mobile laser scanning applications and key techniques over urban areas. Key words: simultaneous localization and mapping, tightly coupled, feature extraction, point cloud registration, global optimization Moreover, the absolute coordinate error of generated point cloud by the proposed method in the open park scene was less than 5 cm, which demonstrates the proposed method can fulfill the requirements of centimeter-level urban mapping. Compared to LIO-SAM, the absolute position error (APE) of the proposed method has improved by 32.25% without the GNSS position factor and has improved by 92.03% with the GNSS position factor (APE<10 cm). The LIO-SAM and proposed method have successfully realized the mapping of all scenes. The test results showed that LOAM and LeGO-LOAM have poor stabilities in complex urban scenes. This study compared the proposed method with three mainstream SLAM methods (i.e., LOAM, LeGO-LOAM, and LIO-SAM) in four common urban scenes (i.e., open park, underground garage, urban park, and road). In addition, a GNSS corner-based constraint was used to improve the accuracy of the global map construction. MAPPING TIME CARTOGRAPHICA REGISTRATIONThe proposed method achieved high accuracy point cloud registration by adding pole-like and plane features that reduced cumulative errors in SLAM. Aiming to reduce the cumulative error and improve the robustness of SLAM system in accurate urban mapping, a tightly coupled laser SLAM algorithm that combined LiDAR, inertial measurement unit (IMU), and global navigation satellite system (GNSS) was developed. ![]()
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