GNSS失锁下融合描述子与粒子滤波的LiDAR定位方法研究

doi:10.13474/j.cnki.11-2246.2023.0237

测绘通报 ›› 2023, Vol. 0 ›› Issue (8): 84-90.doi: 10.13474/j.cnki.11-2246.2023.0237

GNSS失锁下融合描述子与粒子滤波的LiDAR定位方法研究

李昂¹, 钟若飞¹, 刘正军², 谢东海¹, 吴伟³, 张琰¹

1. 三维信息获取与应用教育部重点实验室, 北京 100048;
2. 中国测绘科学研究院, 北京 100039;
3. 清华大学车辆与运载学院, 北京 100084

收稿日期:2022-11-28 发布日期:2023-09-01
通讯作者: 钟若飞。E-mail:zrfsss@163.com
作者简介:李昂(1997-),男,硕士,研究方向为激光LiDAR定位。E-mail:2200902182@cnu.edu.cn
基金资助:
国家自然科学基金(U22A20568；42071444)；国家重点研发计划(2022YFB3904101)

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

LI Ang¹, ZHONG Ruofei¹, LIU Zhengjun², XIE Donghai¹, WU Wei³, ZHANG Yan¹

1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;
2. Chinese Academy of Surveying & Mapping, Beijing 100039, China;
3. Tsinghua University, School of Vehicle and Mobility, Beijing 100084, China

Received:2022-11-28 Published:2023-09-01

摘要/Abstract

摘要： 在GNSS失锁情况下,如何基于三维激光雷达进行定位,是值得关注的问题。通过点云匹配或蒙特卡洛定位(MCL)的方法进行定位,存在初值敏感、计算时间长和搜索空间大的问题。本文提出了一种定位方法,首先利用描述子初始化,确定粗略的位姿,缩小搜索空间,然后使用MCL进行精定位。通过给定描述子初值,预先计算三维网格概率,达到了缩小搜索空间、减少计算时间的效果。初步的试验结果表明,该方法在GNSS失锁情况下定位精度保持较好。

关键词: LiDAR, 描述子, 蒙特卡洛定位, GNSS失锁

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

中图分类号:

P228

李昂, 钟若飞, 刘正军, 谢东海, 吴伟, 张琰. GNSS失锁下融合描述子与粒子滤波的LiDAR定位方法研究[J]. 测绘通报, 2023, 0(8): 84-90.

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.

参考文献

[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.

GNSS失锁下融合描述子与粒子滤波的LiDAR定位方法研究

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

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