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.

翻译：利用激光雷达传感器通过大量遥感数据生成的点云地图，通常被自动驾驶车辆和机器人用于定位和导航。然而，点云地图中包含的动态对象不仅降低了定位精度和导航性能，还损害了地图质量。针对这一挑战，本文提出了一种新颖的基于激光雷达点云的动态场景语义SLAM方法，以下简称SD-SLAM。本工作的主要贡献体现在三个方面：1）引入了一种专门针对动态场景的基于激光雷达点云的语义SLAM框架；2）利用语义信息和卡尔曼滤波有效区分动态和半静态地标；3）在SD-SLAM过程中充分利用具有语义信息的半静态和纯静态地标，以提升定位和建图性能。为评估所提出的SD-SLAM，我们使用广泛采用的KITTI里程计数据集进行了测试。结果表明，所提出的SD-SLAM有效缓解了动态对象对SLAM的不利影响，提升了车辆在动态场景中的定位和建图性能，同时构建了包含多个语义类别的静态语义地图，以增强对环境的理解。