MLO: Multi-Object Tracking and Lidar Odometry in Dynamic Envirnoment

The SLAM system built on the static scene assumption will introduce significant estimation errors when a large number of moving objects appear in the field of view. Tracking and maintaining semantic objects is beneficial to understand the scene and provide rich decision information for planning and control modules. This paper introduces MLO, a multi-object Lidar odometry which tracks ego-motion and movable objects with only the lidar sensor. First, it achieves information extraction of foreground movable objects, surface road, and static background features based on geometry and object fusion perception module. While robustly estimating ego-motion, it accomplishes multi-object tracking through the least-squares method fused by 3D bounding boxes and geometric point clouds. Then, a continuous 4D semantic object map on the timeline can be created. Our approach is evaluated qualitatively and quantitatively under different scenarios on the public KITTI dataset. The experiment results show that the ego localization accuracy of MLO is better than A-LOAM system in highly dynamic, unstructured, and unknown semantic scenes. Meanwhile, the multi-object tracking method with semantic-geometry fusion also has apparent advantages in accuracy and tracking robustness compared with the single method.