许宇伟, 颜文旭, 吴炜. 相似场景下基于局部地图的激光SLAM前端算法改进[J]. 机器人, 2022, 44(2): 176-185.DOI: 10.13973/j.cnki.robot.200541.
XU Yuwei, YAN Wenxu, WU Wei. Improvement of LiDAR SLAM Front-end Algorithm Based on Local Mapin Similar Scenes. ROBOT, 2022, 44(2): 176-185. DOI: 10.13973/j.cnki.robot.200541.
Abstract：In the corridor,tunnel and other similar scenes,performances of traditional LiDAR SLAM (simultaneous localization and mapping) algorithms will seriously degrade,and the algorithms might even be completely invalid due to the similarity of observation data.To solve this problem,the motion prediction model is improved firstly with the hdl_graph_slam algorithm based on the assumption of uniform motion to obtain a more accurate initial pose estimation.Then,the concept of local map is introduced to densify the point cloud,and the performances of the front-end odometer are improved in the similar scenes.In the indoor experiment,the average restoration rate of the scene reaches 99.54%,which is 57.25% higher than that before improvement.In the outdoor experiment,the odometer drift is reduced from 111.62 m to 7.65 m by the improved algorithm.The experimental results show that the proposed algorithm can bring a significant performance improvement in both indoor and outdoor similar scenes.
[1] 危双丰,庞帆,刘振彬,等. 基于激光雷达的同时定位与地图构建方法综述[J].计算机应用研究, 2020, 37(2):327-332. Wei S F, Pang F, Liu Z B, et al. Survey of LiDAR-based SLAM algorithm[J]. Application Research of Computers, 2020, 37(2):327-332. [2] Kohlbrecher S, von Stryk O, Meyer J, et al. A flexible and scalable SLAM system with full 3D motion estimation[C]//IEEE International Symposium on Safety, Security, and Rescue Robotics. Piscataway, USA:IEEE, 2011:155-160. [3] Olson E B. Real-time correlative scan matching[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2009. DOI:10.1109/ROBOT.2009.5152375. [4] Besl P J, McKay N D. A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2):239-256. [5] Biber P, Strasser W. The normal distributions transform:A new approach to laser scan matching[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA:IEEE, 2003:2743-2748. [6] Zhang J, Singh S. Low-drift and real-time lidar odometry and mapping[J]. Autonomous Robot, 2017, 41:401-416. [7] Shan T X, Englot B. LeGO-LOAM:Lightweight and groundoptimized lidar odometry and mapping on variable terrain[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA:IEEE, 2018:4758-4765. [8] Koide K, Miura J, Menegatti E. A portable three-dimensional LIDAR-based system for long-term and wide-area people behavior measurement[J]. International Journal of Advanced Robotic Systems, 2019, 16(2). DOI:10.1177/1729881419841532. [9] 方正,赵世博,李昊来. 一种融合稀疏几何特征与深度流的深度视觉SLAM算法[J].机器人, 2019, 41(2):185-196,241. Fang Z, Zhao S B, Li H L. A depth vision SLAM algorithm combining sparse geometric features with range flow[J]. Robot, 2019, 41(2):185-196,241. [10] Ren Z L, Wang L G, Bi L. Robust GICP-based 3D LiDAR SLAM for underground mining environment[J]. Sensors, 2019, 19(13). DOI:10.3390/s19132915. [11] Segal A V, Haehnel D, Thrun S. Generalized-ICP[C]//Robotics:Science and Systems V. Seattle, USA:University of Washington, 2009. DOI:10.15607/RSS.2009.V.021. [12] 邹谦. 基于图优化SLAM的移动机器人导航方法研究[D]. 哈尔滨:哈尔滨工业大学, 2016. Zou Q. Research on mobile robot navigation method based on graph optimization SLAM[D]. Harbin:Harbin Institute of Technology, 2016. [13] 陈林. 面向隧道内自主侦察无人机系统设计[D].绵阳:西南科技大学, 2019. Chen L. Design of autonomous investigation UAV system for tunnel[D]. Mianyang:Southwest University of Science and Technology, 2019. [14] Lv J J, Xu J H, Hu K W, et al. Targetless calibration of LiDAR-IMU system based on continuous-time batch estimation[DB/OL]. (2020-07-29)[2020-10-15]. https://arxiv.org/abs/2007.14759. [15] Stanford Artificial Intelligence Laboratory. Robotic operating system[EB/OL].[2020-11-29]. https://www.ros.org.
