Research on LiDAR Positioning method of fusion descriptor and particle filter when GNSS loss of lock

doi:10.13474/j.cnki.11-2246.2023.0237

Abstract

Abstract: When GNSS loses lock, how to locate based on 3D LiDAR is a problem worthy of attention. Positioning by point cloud matching or Monte Carlo method has the problems of sensitivity to initial value, long calculation time and large search space. This paper proposes a two-step localization method, which first uses descriptor initialization, determines the rough pose, narrows the search space, and then uses Monte Carlo for fine localization. The use of descriptors in the method gives the initial value, reduces the search space, and pre-computes the three-dimensional grid probability, which reduces the computation time. Preliminary experimental results show that the positioning accuracy remains good in the case of GNSS loss of lock.

Key words: LiDAR, descriptor, Monte Carlo localization, GNSS loss of lock

CLC Number:

P228

LI Ang, ZHONG Ruofei, LIU Zhengjun, XIE Donghai, WU Wei, ZHANG Yan. Research on LiDAR Positioning method of fusion descriptor and particle filter when GNSS loss of lock[J]. Bulletin of Surveying and Mapping, 2023, 0(8): 84-90.

References

[1] JO H,KIM E. New Monte Carlo localization using deep initialization:a three-dimensional LiDAR and a camera fusion approach[J]. IEEE Access,2020,8:74485-74496.
[2] SEGAL A,HAEHNEL D,THRUN S. Generalized-ICP[J].Robotics:Science and Systems. 2009,2(4):435.
[3] KIM H,SONG S,MYUNG H. GP-ICP:ground plane ICP for mobile robots[J]. IEEE Access,2019,7:76599-76610.
[4] BIBER P,STRASSER W. The normal distributions transform:a new approach to laser scan matching[C]//Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems.Las Vegas:IEEE,2004:2743-2748.
[5] JAVANMARDI E,JAVANMARDI M,GU Yanlei,et al. Factors to evaluate capability of map for vehicle localization[J]. IEEE Access,2018,6:49850-49867.
[6] THRUN S. Probabilistic robotics[J]. Communications of the ACM,2002,45(3):52-57.
[7] LEVINSON J,THRUN S. Robust vehicle localization in urban environments using probabilistic maps[C]//Proceedings of 2010 IEEE International Conference on Robotics and Automation. Anchorage:IEEE,2010:4372-4378.
[8] ROFER T,JUNGEL M. Vision-based fast and reactive Monte-Carlo localization[C]//Proceedings of 2003 IEEE International Conference on Robotics and Automation.Taipei:IEEE,2003:856-861.
[9] HATA A Y,WOLF D F. Feature detection for vehicle localization in urban environments using a multilayer LiDAR[J]. IEEE Transactions on Intelligent Transportation Systems,2016,17(2):420-429.
[10] KÜMMERLE R,TRIEBEL R,PFAFF P,et al. Monte Carlo localization in outdoor terrains using multilevel surface maps[J]. Journal of Field Robotics,2008,25(6/7):346-359.
[11] SAARINEN J,ANDREASSON H,STOYANOV T,et al. Normal distributions transform Monte-Carlo localization (NDT-MCL)[C]//Proceedings of 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Tokyo:IEEE,2014:382-389.
[12] KIM H,LIU Bingbing,GOH C Y,et al. Robust vehicle localization using entropy-weighted particle filter-based data fusion of vertical and road intensity information for a large scale urban area[J]. IEEE Robotics and Automation Letters,2017,2(3):1518-1524.
[13] SUHR J K,JANG J,MIN D,et al. Sensor fusion-based low-cost vehicle localization system for complex urban environments[J]. IEEE Transactions on Intelligent Transportation Systems,2017,18(5):1078-1086.
[14] CASTORENA J,AGARWAL S. Ground-edge-based LiDAR localization without a reflectivity calibration for autonomous driving[J]. IEEE Robotics and Automation Letters,2018,3(1):344-351.
[15] SU Zerong,ZHOU Xuefeng,CHENG Taobo,et al. Global localization of a mobile robot using LiDAR and visual features[C]//Proceedings of 2018 IEEE International Conference on Robotics and Biomimetics.Macao:IEEE,2018:2377-2383.
[16] CASELITZ T,STEDER B,RUHNKE M,et al. Monocular camera localization in 3D LiDAR maps[C]//Proceedings of 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Daejeon:IEEE,2016:1926-1931.
[17] ROZENBERSZKI D,MAJDIK A L. LOL:LiDAR-only Odometry and Localization in 3D point cloud maps[C]//Proceedings of 2020 IEEE International Conference on Robotics and Automation (ICRA). Paris:IEEE,2020:4379-4385.
[18] BLANCO-CLARACO J L,MAÑAS-ALVAREZ F,TORRES-MORENO J L,et al. Benchmarking particle filter algorithms for efficient velodyne-based vehicle localization[J]. Sensors,2019,19(14):3155.
[19] JIN Ye,CHEN Qinying,QIAN Jie,et al. Global localization for single 3D point cloud using voting mechanism[C]//Proceedings of the 6th IEEE International Conference on Advanced Robotics and Mechatronics. Chongqing:IEEE,2021:771-776.
[20] PEREZ-GRAU F J,CABALLERO F,VIGURIA A,et al. Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation[J]. International Journal of Advanced Robotic Systems,2017,14(5):172988141773275.
[21] HE Li,WANG Xiaolong,ZHANG Hong. M2DP:a novel 3D point cloud descriptor and its application in loop closure detection[C]//Proceedings of 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems.Daejeon:IEEE,2016:231-237.
[22] CHA S H. Taxonomy of nominal type histogram distance measures[C]//Proceedings of 2008 American Conference on Applied Mathematics. New York:ACM press,2008:325-330.
[23] MUHAMMAD N,LACROIX S. Loop closure detection using small-sized signatures from 3D LiDAR data[C]//Proceedings of 2011 IEEE International Symposium on Safety,Security,and Rescue Robotics. Kyoto:IEEE,2011:333-338.
[24] MOAKHER M. Means and averaging in the group of rotations[J]. SIAM Journal on Matrix Analysis and Applications,2002,24(1):1-16.