  Simultaneous localization and mapping (SLAM) is critical to the
implementation of 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. We propose a trajectory-based dynamic feature
filtering method, which filters out features belonging to moving objects by
leveraging tracking results 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 https://github.com/tiev-tongji/LIMOT.
