For the SLAM system in robotics and autonomous driving, the accuracy of front-end odometry and back-end loop-closure detection determine the whole intelligent system performance. But the LiDAR-SLAM could be disturbed by current scene moving objects, resulting in drift errors and even loop-closure failure. Thus, the ability to detect and segment moving objects is essential for high-precision positioning and building a consistent map. In this paper, we address the problem of moving object segmentation from 3D LiDAR scans to improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D Sequential Moving-Object-Segmentation (3D-SeqMOS) method that can accurately segment the scene into moving and static objects, such as moving and static cars. Different from the existing projected-image method, we process the raw 3D point cloud and build a 3D convolution neural network for MOS task. In addition, to make full use of the spatio-temporal information of point cloud, we propose a point cloud residual mechanism using the spatial features of current scan and the temporal features of previous residual scans. Besides, we build a complete SLAM framework to verify the effectiveness and accuracy of 3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed 3D-SeqMOS method can effectively detect moving objects and improve the accuracy of LiDAR odometry and loop-closure detection. The test results show our 3D-SeqMOS outperforms the state-of-the-art method by 12.4%. We extend the proposed method to the SemanticKITTI: Moving Object Segmentation competition and achieve the 2nd in the leaderboard, showing its effectiveness.

翻译：在机器人学和自动驾驶的SLAM系统中，前端里程计与后端回环检测的精度决定了整个智能系统的性能。然而，LiDAR-SLAM易受当前场景中运动物体的干扰，导致漂移误差甚至回环检测失败。因此，检测并分割运动物体的能力对于高精度定位和构建一致性地图至关重要。本文针对从三维LiDAR扫描中分割运动物体的问题，旨在提升SLAM的里程计与回环检测精度。我们提出了一种新颖的三维时序运动物体分割方法（3D-SeqMOS），该方法能够将场景准确分割为运动物体与静态物体（例如运动与静止的车辆）。与现有基于投影图像的方法不同，我们直接处理原始三维点云，并构建三维卷积神经网络用于MOS任务。此外，为充分利用点云的时空信息，我们提出了一种点云残差机制，该机制结合当前扫描的空间特征与先前残差扫描的时序特征。同时，我们构建了完整的SLAM框架以验证3D-SeqMOS的有效性与精度。在SemanticKITTI数据集上的实验表明，所提出的3D-SeqMOS方法能够有效检测运动物体，并提升LiDAR里程计与回环检测的精度。测试结果显示，3D-SeqMOS较当前最先进方法提升了12.4%的性能。我们将该方法拓展至SemanticKITTI：运动物体分割竞赛，并取得排行榜第二名的成绩，验证了其有效性。