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Lidar Inertial Odometry and Mapping for Autonomous Vehicle in GPS-Denied Parking Lot
Technical Paper
2020-01-0103
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
High-precision and real-time ego-motion estimation is vital for autonomous vehicle. There is a lot GPS-denied maneuver such as underground parking lot in urban areas. Therefore, the localization system relying solely on GPS cannot meets the requirements. Recently, lidar odometry and visual odometry have been introduced into localization systems to overcome the problem of missing GPS signals. Compared with visual odometry, lidar odometry is not susceptible to light, which is widely applied in weak-light environments. Besides, the autonomous parking is highly dependent on the geometric information around the vehicle, which makes building map of surroundings essential for autonomous vehicle. We propose a lidar inertial odometry and mapping. By sensor fusion, we compensate for the drawback of applying a single sensor, allowing the system to provide a more accurate estimate. Compared to other odometry using IMU and lidar, we apply a tight coupled of lidar and IMU method to achieve lower drift, which can effectively overcome the degradation problem based on pure lidar method, ensuring precise pose estimation in fast motion. In addition, we propose a map update method to ensure the real-time performance of the mapping, which meets the high requirements of the autonomous parking in the dynamic environment. At the back end, we apply a global pose optimization to reduce the drift of the pose over time. The experimental results demonstrate that the proposed odometry can stably estimate the pose in high dynamic and degraded underground parking lot in real-time. Besides, the proposed mapping method can update the map stably and reduce the drift of pose over time.
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Citation
Chen, X., Zhang, S., Wu, J., He, R. et al., "Lidar Inertial Odometry and Mapping for Autonomous Vehicle in GPS-Denied Parking Lot," SAE Technical Paper 2020-01-0103, 2020, https://doi.org/10.4271/2020-01-0103.Data Sets - Support Documents
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References
- Besl , P.J. and McKay , N.D. A Method for Registration of 3D Shapes IEEE Transactions on Pattern Analysis and Machine Intelligence 14 2 239 256 1992
- Censi , A.
- Low , K.-L.
- Segal , V. Haehnel , D. , and Thrun , S. Generalized-ICP Generalized-ICP 2009
- Rusu , R.B. , Marton , Z.C. , Blodow , N. , and Beetz , M. Learning Informative Point Classes for the Acquisition of Object Model Maps 2008 10th International Conference on Control, Automation, Robotics and Vision, IEEE Hanoi Vietnam 643 650 2008 10.1109/ICARCV.2008.4795593
- Rusu , R.B. , Bradski , G. , Thibaux , R. , and Hsu , J. Fast 3D Recognition and Pose Using the Viewpoint Feature Histogram 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE Taipei 2155 2162 2010 10.1109/IROS.2010.5651280
- Zhang , J. Low-Drift and Real-Time Lidar Odometry and Mapping Auton Robot 16 2017
- Velas , M. , Spanel , M. , and Herout , A. Collar Line Segments for Fast Odometry Estimation from Velodyne Point Clouds 2016 IEEE International Conference on Robotics and Automation (ICRA) , IEEE Stockholm Sweden 4486 4495 2016 10.1109/ICRA.2016.7487648
- Shan , T. and Englot , B. LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , IEEE Madrid 4758 4765 2018 10.1109/IROS.2018.8594299
- Soloviev , A. , Bates , D. , and van Graas , F. Tight Coupling of Laser Scanner and Inertial Measurements for a Fully Autonomous Relative Navigation Solution 2007
- Hemann , G. , Singh , S. , and Kaess , M. Long-Range GPS-Denied Aerial Inertial Navigation with LIDAR Localization 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 1659 1666 2016
- Ye , H. , Chen , Y. , and Liu , M. Tightly Coupled 3D Lidar Inertial Odometry and Mapping 2019 International Conference on Robotics and Automation (ICRA) 3144 3150 2019 10.1109/ICRA.2019.8793511
- Geneva , P. , Eckenhoff , K. , Yang , Y. , and Huang , G. LIPS: LiDAR-Inertial 3D Plane SLAM 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , IEEE Madrid 123 130 2018 10.1109/IROS.2018.8594463
- Qin , C. , Ye , H. , Pranata , C.E. , Han , J. , Zhang , S. , and Liu , M. 2019
- Qin , T. , Li , P. , and Shen , S. VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator IEEE Transactions on Robotics 34 1004 1020 2017
- Himmelsbach , M. , von Hundelshausen , F. , and Wünsche , H.-J. Fast Segmentation of 3D Point Clouds for Ground Vehicles 2010 IEEE Intelligent Vehicles Symposium 2010 560 565
- Bogoslavskyi , I. and Stachniss , C. Fast Range Image-Based Segmentation of Sparse 3D Laser Scans for Online Operation 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 163 169 2016
- Sibley , G. A Sliding Window Filter for SLAM 17
- Agarwal , S. , Mierle , K. , et al. Ceres Solver