  Point cloud maps generated via LiDAR sensors using extensive remotely sensed
data are commonly used by autonomous vehicles and robots for localization and
navigation. However, dynamic objects contained in point cloud maps not only
downgrade localization accuracy and navigation performance but also jeopardize
the map quality. In response to this challenge, we propose in this paper a
novel semantic SLAM approach for dynamic scenes based on LiDAR point clouds,
referred to as SD-SLAM hereafter. The main contributions of this work are in
three aspects: 1) introducing a semantic SLAM framework dedicatedly for dynamic
scenes based on LiDAR point clouds, 2) Employing semantics and Kalman filtering
to effectively differentiate between dynamic and semi-static landmarks, and 3)
Making full use of semi-static and pure static landmarks with semantic
information in the SD-SLAM process to improve localization and mapping
performance. To evaluate the proposed SD-SLAM, tests were conducted using the
widely adopted KITTI odometry dataset. Results demonstrate that the proposed
SD-SLAM effectively mitigates the adverse effects of dynamic objects on SLAM,
improving vehicle localization and mapping performance in dynamic scenes, and
simultaneously constructing a static semantic map with multiple semantic
classes for enhanced environment understanding.
