一种面向室内动态行人场景的激光SLAM算法

doi:10.19678/j.issn.1000-3428.0067675

摘要/Abstract

摘要： 在复杂室内环境中,消除动态行人对实时建图的干扰一直是激光同步定位与建图(SLAM)算法需要解决的核心问题之一。当前的SLAM算法主要关注静态场景,忽略了场景中存在的运动物体。然而,在室内场景中,频繁出现的移动行人降低了全局点云地图的质量,增加了后续定位与导航的不确定性。提出一种针对室内动态行人场景的紧耦合激光SLAM算法,以更好地适应此类复杂场景。在传统SLAM框架的基础上引入基于点云聚类与分割的预处理模块,用于准确消除动态行人点云。该算法首先采用基于欧氏距离的增强两步式聚类算法对点云进行聚类和分割,随后提取聚类结果的多维切片特征和强度特征,并结合支持向量机(SVM)的分类结果来识别场景中的行人实例,同时利用静态点云实时估计自身位姿并构建高分辨率点云地图。分别使用Hilti公开数据集以及真实场景数据对所提算法的动态点云去除效果和实时性进行测试,结果表明,相较于Removert、Dynablox等当前先进的激光SLAM算法,该算法能够显著改善点云地图的构建效果,降低其中动态行人点云的比例,且系统对单帧图片的处理时长不超过100 ms,满足实时性要求。

关键词: 同步定位与建图, 多传感器融合, 动态行人, 紧耦合, 点云处理

Abstract: Eliminating the interference of dynamic pedestrians in real-time mapping is a core challenge in laser Simultaneous Localization And Mapping (SLAM) algorithms, particularly in complex indoor environments. Most existing SLAM algorithms focus primarily on static scenes and overlook the presence of moving objects. However, in indoor environments, the frequent appearance of moving pedestrians significantly degrades the quality of the global point-cloud map and increases uncertainty in subsequent localization and navigation tasks. To address this issue, this study proposes a tightly coupled laser SLAM algorithm specifically designed for dynamic pedestrian scenarios in indoor environments, with the aim of better adapting to such complex scenarios. In addition to the traditional SLAM framework, this study introduces a pre-processing module based on point-cloud clustering and segmentation to accurately eliminate dynamic pedestrian point clouds. Our algorithm first applies an enhanced two-stage clustering algorithm based on the Euclidean distance to cluster and segment point clouds. Subsequently, multidimensional slice and intensity features are extracted from the clustering results and combined with the classification results of a Support Vector Machine (SVM) to identify pedestrian instances at the scene. Meanwhile, the algorithm utilizes a static point cloud to estimate ego motion in real time and constructs a high-resolution point cloud map. To evaluate the performance of the algorithm, assessments are performed on both the Hilti public dataset and real-world scenario data, specifically focusing on the effectiveness of dynamic point-cloud removal and real-time capability. Experimental results demonstrate that the algorithm significantly improves the point cloud map construction quality and remarkably reduces the proportion of dynamic pedestrian points compared to state-of-the-art laser SLAM algorithms such as Removert and Dynablox. The processing time of the system for a single frame image does not exceed 100 ms, meeting real-time requirements.

Key words: Simultaneous Localization And Mapping(SLAM), multi-sensor fusion, dynamic pedestrian, tightly coupling, point cloud processing

中图分类号:

TP391

叶智奇, 章国宝, 朱宏伟. 一种面向室内动态行人场景的激光SLAM算法[J]. 计算机工程, 2024, 50(6): 208-217.

YE Zhiqi, ZHANG Guobao, ZHU Hongwei. A Laser SLAM Algorithm for Indoor Dynamic Pedestrian Scenes[J]. Computer Engineering, 2024, 50(6): 208-217.

https://www.ecice06.com/CN/Y2024/V50/I6/208

参考文献

[1] 周治国,曹江微,邸顺帆. 3D激光雷达SLAM算法综述[J].仪器仪表学报, 2021, 42(9):13-27. ZHOU Z G, CAO J W, DI S F. Overview of 3D lidar SLAM algorithms[J]. Chinese Journal of Scientific Instrument, 2021, 42(9):13-27.(in Chinese)
[2] 曾庆化,罗怡雪,孙克诚,等.视觉及其融合惯性的SLAM技术发展综述[J].南京航空航天大学学报, 2022, 54(6):1007-1020. ZENG Q H, LUO Y X, SUN K C, et al. Review on SLAM technology development for vision and its fusion of inertial information[J]. Journal of Nanjing University of Aeronautics&Astronautics, 2022, 54(6):1007-1020.(in Chinese)
[3] ZHANG J Y, YAO Y X, DENG B L. Fast and robust iterative closest point[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(7):3450-3466.
[4] SEGAL A, HAEHNEL D, THRUN S. Generalized-ICP[EB/OL].[2023-04-05]. https://www.semanticscholar.org/paper/Generalized-ICP-Segal-H%C3% A4hnel/b352b3a7f1068b2d562ba 12a446628397dfe8a77.
[5] ZHANG J, SINGH S. LOAM:lidar odometry and mapping in real-time[EB/OL].[2023-04-05].https://www.researchgate.net/publication/282704722_LOAM_Lidar_Odometry_and_Mapping_in_Real-time.
[6] 王金科,左星星,赵祥瑞,等.多源融合SLAM的现状与挑战[J].中国图象图形学报, 2022, 27(2):368-389. WANG J K, ZUO X X, ZHAO X R, et al. Review of multi-source fusion SLAM:current status and challenges[J]. Journal of Image and Graphics, 2022, 27(2):368-389.(in Chinese)
[7] SHAN T X, 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. New York,USA:ACM Press,2018:4758-4765.
[8] YE H Y, CHEN Y Y, LIU M. Tightly coupled 3D lidar inertial odometry and mapping[C]//Proceedings of International Conference on Robotics and Automation. Washington D.C.,USA:IEEE Press,2019:3144-3150.
[9] SHAN T X, ENGLOT B, MEYERS D, et al. LIO-SAM:tightly-coupled lidar inertial odometry via smoothing and mapping[C]//Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems. Washington D.C.,USA:IEEE Press,2020:5135-5142.
[10] XU W, ZHANG F. FAST-LIO:a fast, robust LiDAR-inertial odometry package by tightly-coupled iterated Kalman filter[J]. IEEE Robotics and Automation Letters, 2021, 6(2):3317-3324.
[11] 张金凤,石朝侠,王燕清.动态场景下基于视觉特征的SLAM方法[J].计算机工程, 2020, 46(10):95-102. ZHANG J F, SHI C X, WANG Y Q. SLAM method based on visual features in dynamic scene[J]. Computer Engineering, 2020, 46(10):95-102.(in Chinese)
[12] SCHAUER J, NUCHTER A. The peopleremover-removing dynamic objects from 3-D point cloud data by traversing a voxel occupancy grid[J]. IEEE Robotics and Automation Letters, 2018, 3(3):1679-1686.
[13] QIAN C, XIANG Z, WU Z, et al. RF-LIO:removal-first tightly-coupled lidar inertial odometry in high dynamic environments[EB/OL].[2023-04-05].https://arxiv.org/abs/2206.09463.
[14] PFREUNDSCHUH P, HENDRIKX H F C, REIJGWART V, et al. Dynamic object aware LiDAR SLAM based on automatic generation of training data[C]//Proceedings of IEEE International Conference on Robotics and Automation. Washington D.C.,USA:IEEE Press,2021:11641-11647.
[15] THRUN S. Probabilistic robotics[J]. Communications of the ACM, 2002, 45(3):52-57.
[16] HASELICH M, JOBGEN B, WOJKE N, et al. Confidence-based pedestrian tracking in unstructured environments using 3D laser distance measurements[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. Washington D.C.,USA:IEEE Press,2014:4118-4123.
[17] ARTHUR D, VASSILVITSKII S. K-means++the advantages of careful seeding[C]//Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms. New York,USA:ACM Press,2007:1027-1035.
[18] PREMEBIDA C, LUDWIG O, NUNES U. Exploiting LIDAR-based features on pedestrian detection in urban scenarios[C]//Proceedings of the 12th IEEE International Conference on Intelligent Transportation Systems. Washington D.C.,USA:IEEE Press,2009:1-6.
[19] KIDONO K, MIYASAKA T, WATANABE A, et al. Pedestrian recognition using high-definition LIDAR[C]//Proceedings of IEEE Intelligent Vehicles Symposium. Washington D.C.,USA:IEEE Press,2011:405-410.
[20] SOLA J. Quaternion kinematics for the error-state Kalman filter[EB/OL].[2023-04-05].https://arxiv.org/abs/1711.02508.
[21] XU W, CAI Y X, HE D J, et al. FAST-LIO2:fast direct LiDAR-inertial odometry[J]. IEEE Transactions on Robotics, 2022, 38(4):2053-2073.
[22] ZHANG L T, HELMBERGER M, FU L F T, et al. Hilti-Oxford dataset:a millimeter-accurate benchmark for simultaneous localization and mapping[J]. IEEE Robotics and Automation Letters, 2023, 8(1):408-415.
[23] LIM H, HWANG S, MYUNG H. ERASOR:egocentric ratio of pseudo occupancy-based dynamic object removal for static 3D point cloud map building[J]. IEEE Robotics and Automation Letters, 2021, 6(2):2272-2279.
[24] KIM G, KIM A. Remove, then revert:static point cloud map construction using multiresolution range images[C]//Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems. Washington D.C.,USA:IEEE Press,2020:10758-10765.
[25] SCHMID L, ANDERSSON O, SULSER A, et al. Dynablox:real-time detection of diverse dynamic objects in complex environments[EB/OL].[2023-04-05].https://arxiv.org/abs/2304.10049.
[26] FAN T X, SHEN B W, CHEN H, et al. DynamicFilter:an online dynamic objects removal framework for highly dynamic environments[C]//Proceedings of International Conference on Robotics and Automation. Washington D.C.,USA:IEEE Press,2022:7988-7994.

选择文件类型/文献管理软件名称

选择包含的内容