Existing 3D point-based dynamic point detection and removal methods have a significant time overhead, making them difficult to adapt to LiDAR-inertial odometry systems. This paper proposes a label consistency based dynamic point detection and removal method for handling moving vehicles and pedestrians in autonomous driving scenarios, and embeds the proposed dynamic point detection and removal method into a self-designed LiDAR-inertial odometry system. Experimental results on three public datasets demonstrate that our method can accomplish the dynamic point detection and removal with extremely low computational overhead (i.e., 1$\sim$9ms) in LIO systems, meanwhile achieve comparable preservation rate and rejection rate to state-of-the-art methods and significantly enhance the accuracy of pose estimation. We have released the source code of this work for the development of the community.

翻译：现有的基于三维点的动态点检测与去除方法具有显著的时间开销，难以适配激光雷达-惯性里程计系统。本文提出一种基于标签一致性的动态点检测与去除方法，用于处理自动驾驶场景中的移动车辆与行人，并将所提出的动态点检测与去除方法嵌入至自主设计的激光雷达-惯性里程计系统中。在三个公开数据集上的实验结果表明，我们的方法能够在激光雷达-惯性里程计系统中以极低计算开销（即1$\sim$9毫秒）完成动态点检测与去除，同时达到与先进方法相当的保留率与剔除率，并显著提升了位姿估计的精度。我们已开源本工作的源代码以促进社区发展。