周光召, 苑晶, 高海明, 孙沁璇, 张雪波, 俞诗卓. 结构化环境下基于结构单元软编码的3维激光雷达点云描述子[J]. 机器人, 2020, 42(6): 641-650. DOI: 10.13973/j.cnki.robot.190657
引用本文: 周光召, 苑晶, 高海明, 孙沁璇, 张雪波, 俞诗卓. 结构化环境下基于结构单元软编码的3维激光雷达点云描述子[J]. 机器人, 2020, 42(6): 641-650. DOI: 10.13973/j.cnki.robot.190657
ZHOU Guangzhao, YUAN Jing, GAO Haiming, SUN Qinxuan, ZHANG Xuebo, YU Shizhuo. The 3D Lidar Point Cloud Descriptor Based on Structural Unit Soft-Encoding in Structured Environment[J]. ROBOT, 2020, 42(6): 641-650. DOI: 10.13973/j.cnki.robot.190657
Citation: ZHOU Guangzhao, YUAN Jing, GAO Haiming, SUN Qinxuan, ZHANG Xuebo, YU Shizhuo. The 3D Lidar Point Cloud Descriptor Based on Structural Unit Soft-Encoding in Structured Environment[J]. ROBOT, 2020, 42(6): 641-650. DOI: 10.13973/j.cnki.robot.190657

结构化环境下基于结构单元软编码的3维激光雷达点云描述子

The 3D Lidar Point Cloud Descriptor Based on Structural Unit Soft-Encoding in Structured Environment

  • 摘要: 在同时定位与地图构建(SLAM)系统中,基于3维激光雷达点云数据的闭环检测由于描述子计算困难而极具挑战.为此,本文提出一种结构化环境下可用于闭环检测的基于结构单元软编码的新型3维激光雷达点云描述子.针对3维激光雷达点云的稀疏性和独立性导致的3维空间线段提取困难的问题,首先通过几何滤波的方法提取3维空间中垂直于地面的线段,用于保留3维空间的结构信息;然后,基于线段的空间几何关系构建结构单元集合,并通过软编码技术计算特征向量,作为3维激光雷达点云的描述子;最后,通过两帧点云描述子的匹配实现闭环检测.在KITTI公开数据集和自采数据集上的对比实验,验证了本文方法在时效性和鲁棒性等方面均优于主流的3维激光闭环检测方法.

     

    Abstract: Due to the difficulty in descriptor calculation, loop closure detection using 3D lidar point cloud data is a challenging problem in simultaneous localization and mapping (SLAM) systems. Therefore, a novel 3D lidar point cloud descriptor based on structural unit soft-encoding is proposed in this paper, which can be applied to loop closure detection in structured environments. For the difficulty in extracting 3D line segments caused by the sparsity and independence of 3D lidar point cloud, the line segments perpendicular to the ground are extracted firstly by geometric filtering to preserve the structural information of 3D space. Then, a set of structural units is constructed based on the space geometric relationship among the obtained line segments, and the feature vectors are calculated by soft encoding, which are used as the descriptors of the 3D lidar point cloud, Finally, loop closure detection is achieved through the descriptors matching of the two frames of point cloud. Comparative experiments on the KITTI dataset and self-collected dataset show that the proposed approach presents superior performances over the state-of-the-art 3D lidar loop closure detection methods in terms of effectiveness and robustness.

     

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