A laser SLAM method for unmanned vehicles in point cloud degenerated tunnel environments

doi:10.11947/j.AGCS.2021.20210248

Abstract

Abstract: Laser SLAM enables to locate the vehicle itself even in an unknown environment, and to efficiently sample the three-dimensional geospatial information of the traversed environment, which has been drawn wide attention in the field of autonomous driving in recent years. To improve the accuracy and performance of the laser SLAM system in a point cloud degenerated tunnel environment, we present an intensity enhanced laser SLAM approach based on LOAM. First, we improve the feature extraction module of LOAM. An adaptive feature extraction method based on spherical projection image is presented to extract line, façde, ground and reflectors from a single laser sweep. Besides, to solve the issue on point cloud registration degeneracy in tunnel environments, we presented intensity feature-basedregistration approach to fix the vehicle pose resulting from the geometric feature-based registration error. Reflecting features in the surrounding are adaptively extracted to ensure the adaptivity of our improved laser SLAM approach. The experimental results show that the proposed method presented the better and more robust performance especially in degenerated environments, e.g., long straight tunnel, comparing with the performance of LOAM and HDL-Graph-SLAM. The accuracy of the proposed method was an order of magnitude larger than that of LOAM and HDL-Graph-SLAM.

Key words: laser SLAM, point cloud degeneracy, point feature extraction, point cloud registration

CLC Number:

P232

LI Shuaixin, LI Jiuren, TIAN Bin, CHEN Long, WANG Li, LI Guangyun. A laser SLAM method for unmanned vehicles in point cloud degenerated tunnel environments[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(11): 1487-1499.

References

[1] 宁津生. 测绘科学与技术转型升级发展战略研究[J]. 武汉大学学报(信息科学版), 2019, 44(1): 1-9. NING Jinsheng. Research on the development strategy of surveying and mapping science and technology transformation and upgrading[J]. Geomatics and Information Science of Wuhan University, 2019, 44(1): 1-9.
[2] 李德仁, 龚健雅, 邵振峰. 从数字地球到智慧地球[J]. 武汉大学学报(信息科学版), 2010, 35(2): 127-132, 253. LI Deren, GONG Jianya, SHAO Zhenfeng. From digital earth to smart earth[J]. Geomatics and Information Science of Wuhan University, 2010, 35(2): 127-132, 253.
[3] 卢秀山, 滕腾, 刘如飞. 移动测量、地理信息更新与城市管理智能化[J]. 测绘学报, 2017, 46(10): 1592-1597. DOI: 10.11947/j.AGCS.2017.20170327. LU Xiushan, TENG Teng, LIU Rufei. Mobile mapping, geographic information update and urban management intelligence[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1592-1597. DOI: 10.11947/j.AGCS.2017.20170327.
[4] 陈驰, 杨必胜, 田茂, 等. 车载MMS激光点云与序列全景影像自动配准方法[J]. 测绘学报, 2018, 47(2): 215-224. DOI: 10.11947/j.AGCS.2018.20170520. CHEN Chi, YANG Bisheng, TIAN Mao, et al. Automatic registration of vehicle-borne mobile mapping laser point cloud and sequent panoramas[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(2): 215-224. DOI: 10.11947/j.AGCS.2018.20170520
[5] SINGANDHUPE A, LA H M. A review of SLAM techniques and security in autonomous driving[C]//Proceedings of the 3rd IEEE International Conference on Robotic Computing (IRC). 2019, Naples, Italy: IEEE, 2019: 602-607.
[6] ZAFFAR M, EHSAN S, STOLKIN R, et al. Sensors, SLAM and long-term autonomy: a review[C]//Proceedings of 2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS). Edinburgh, UK: IEEE, 2018: 285-290.
[7] GRISETTI G, STACHNISS C, BURGARD W. Improved techniques for grid mapping with Rao-blackwellized particle filters[J]. IEEE Transactions on Robotics, 2007, 23(1): 34-46.
[8] KOHLBRECHER S, VON STRYK O, MEYER J, et al. A flexible and scalable SLAM system with full 3D motion estimation[C]//Proceedings of 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics. Kyoto, Japan: IEEE, 2011: 155-160.
[9] HESS W, KOHLER D, RAPP H, et al. Real-time loop closure in 2D LIDAR SLAM[C]//Proceedings of 2016 IEEE International Conference on Robotics and Automation (ICRA). Stockholm, Sweden: IEEE, 2016: 1271-1278.
[10] RODRIGUES R T, TSIOGKAS N, AGUIAR A P, et al. B-spline surfaces for range-based environment mapping[C]//Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Las Vegas, NV, USA: IEEE, 2021: 10774-10779.
[11] ZHANG Ji, SINGH S. Low-drift and real-time lidar odometry and mapping[J]. Autonomous Robots, 2017, 41(2): 401-416.
[12] SHAN Tixiao, ENGLOT B. LeGO-LOAM: lightweight and ground-optimized lidar odometry and mapping on variable terrain[C]//Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid, Spain: IEEE, 2018: 4758-4765.
[13] LIN Jiarong, ZHANG Fu. Loam livox: a fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV[C]//Proceedings of 2020 IEEE International Conference on Robotics and Automation (ICRA). Paris, France: IEEE, 2020: 3126-3131.
[14] JIAO Jianhao, YE Haoyang, ZHU Yilong, et al. Robust odometry and mapping for multi-LiDAR systems with online extrinsic calibration[J]. IEEE Transactions on Robotics, 8287, PP(99): 1-10.
[15] MOOSMANN F, STILLER C. Velodyne SLAM[C]//Proceedings of 2011 IEEE Intelligent Vehicles Symposium (IV). Baden-Baden, Germany: IEEE, 2011: 393-398.
[16] KOIDE K, JUN M, EMANUELE M. A portable 3D LIDAR-based system for long-term and wide-area people behavior measurement[J]. International Journal of Advanced Robotic Systems,2019, 16(2): 1-16.
[17] BEHLEY J, STACHNISS C. Efficient surfel-based SLAM using 3D laser range data in urban environments[C]//Proceedings of Robotics: Science and Systems ⅩⅣ. [S.l.]: Science and Systems Foundation, 2018: 16-25.
[18] DROESCHEL D, SCHWARZ M, BEHNKE S. Continuous mapping and localization for autonomous navigation in rough terrain using a 3D laser scanner[J]. Robotics and Autonomous Systems, 2017, 88: 104-115.
[19] TIAN Y, LIU X, LI L, et al. Intensity-assisted ICP for fast registration of 2D-LiDAR[J]. Sensors (Basel, Switzerland), 2019, 19(9). DOI:10.3390/s19092124.
[20] KHAN S, WOLLHERR D, BUSS M. Modeling laser intensities for simultaneous localization and mapping[J]. IEEE Robotics and Automation Letters, 2016, 1(2): 692-699.
[21] WANG Han, WANG Chen, XIE Lihua. Intensity-SLAM: intensity assisted localization and mapping for large scale environment[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1715-1721.
[22] PARK Y S, JANG H, KIM A. I-LOAM: intensity enhanced LiDAR odometry and mapping[C]//Proceedings of the 17th International Conference on Ubiquitous Robots (UR). Kyoto, Japan: IEEE, 2020: 455-458.
[23] GEIGER A, LENZ P, URTASUN R. Are we ready for autonomous driving? The KITTI vision benchmark suite[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA: IEEE, 2012: 3354-3361.
[24] GEIGER A, LENZ P, STILLER C, et al. Vision meets robotics: The KITTI dataset[J]. The International Journal of Robotics Research, 2013, 32(11): 1231-1237.
[25] BOGOSLAVSKYI I, STACHNISS C. Efficient online segmentation for sparse 3D laser scans[J]. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2017, 85(1): 41-52.
[26] BARFOOT T D. State estimation for robotics[M]. Cambridge: Cambridge University Press, 2017.
[27] ZHANG Ji, KAESS M, SINGH S. On degeneracy of optimization-based state estimation problems[C]//Proceedings of 2016 IEEE International Conference on Robotics and Automation (ICRA). Stockholm, Sweden: IEEE, 2016: 809-816.
[28] 李帅鑫, 李广云, 王力, 等. LiDAR/IMU紧耦合的实时定位方法[J]. 自动化学报, 2021, 47(6): 1377-1389. LI Shuaixin, LI Guangyun, WANG Li, et al. LiDAR/IMU tightly coupled real-time localization method[J]. Acta Automatica Sinica, 2021, 47(6): 1377-1389.