LIMOT: A Tightly-Coupled System for LiDAR-Inertial Odometry and Multi-Object Tracking
Zhongyang Zhu, Junqiao Zhao, Kai Huang, Xuebo Tian, Jiaye Lin, Chen Ye
Abstract
Simultaneous localization and mapping (SLAM) is essential for autonomous driving. Most LiDAR-inertial SLAM algorithms assume a static environment, leading to unreliable localization in dynamic environments. Moreover, the accurate tracking of moving objects is of great significance for the control and planning of autonomous vehicles. This study proposes LIMOT, a tightly-coupled multi-object tracking and LiDAR-inertial odometry system that is capable of accurately estimating the poses of both ego-vehicle and objects. Based on the historical trajectories of tracked objects in a sliding window, we perform robust data association. We propose a trajectory-based dynamic feature filtering method, which leverages tracking results to filter out features belonging to moving objects before scan-matching. Factor graph-based optimization is then conducted to optimize the bias of the IMU and the poses of both the ego-vehicle and surrounding objects in a sliding window. Experiments conducted on the KITTI tracking dataset and self-collected dataset show that our method achieves better pose and tracking accuracy than our previous work DL-SLOT and other baseline methods. Our open-source implementation is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tiev-tongji/LIMOT</uri> .