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.

翻译：同步定位与地图构建（SLAM）是自动驾驶实现的关键技术。现有大多数激光雷达-惯性SLAM算法假设处于静态环境，在动态场景中会导致不可靠的定位。此外，运动目标的精确跟踪对自动驾驶车辆的决策控制与路径规划具有重要意义。本研究提出LIMOT——一种紧耦合的多目标跟踪与激光雷达-惯性里程计系统，能够同时准确估计自车与目标的位姿。我们提出基于轨迹的动态特征过滤方法，在扫描匹配前利用跟踪结果滤除属于运动目标的特征。随后执行基于因子图优化的算法，在滑动窗口内优化IMU零偏以及自车与周围目标的位姿。在KITTI跟踪数据集与自采集数据集上的实验表明，我们的方法相比先前工作DL-SLOT及其他基线方法取得了更优的位姿估计与跟踪精度。开源代码见https://github.com/tiev-tongji/LIMOT。