Robust and accurate localization and mapping are
essential for autonomous driving. The traditional SLAM methods
generally work under the assumption that the environment is
static, while in dynamic environment the performance will be
degenerate. In this paper, we propose an efficient and effective
method to eliminate the influence of dynamic environment on
SLAM by fusing LiDAR and mmW-radar, which significantly
improves the robustness and accuracy of localization and mapping.
The method fully utilizes the advantages of different
measurement characteristics of two sensors, efficient moving
object detection based on Doppler effect by radar and accurate
object segmentation and localization by LiDAR, to remove the
moving objects and uses the resulting filtered point cloud as the
input of SLAM towards enhanced performance. The proposed
approach is evaluated through experiments in various real world
scenarios, and the results demonstrate the effectiveness of the
method to improve the robustness and accuracy of SLAM in
dynamic environments.