SD-SLAM：一种基于 LiDAR 点云的动态场景语义 SLAM 方法
SD-SLAM: A Semantic SLAM Approach for Dynamic Scenes Based on LiDAR Point Clouds
AI总结：
本文提出基于 LiDAR 点云的语义 SLAM 框架 SD-SLAM，通过语义与 Kalman 滤波区分动态及半静态地标，并在 KITTI 上验证其可提升动态场景定位建图效果。
摘要：
通过 LiDAR 传感器利用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与导航。然而，点云地图中包含的动态物体不仅会降低定位精度和导航性能，还会损害地图质量。为应对这一挑战，本文提出一种基于 LiDAR 点云、面向动态场景的新型语义 SLAM 方法，以下称为 SD-SLAM。本工作的主要贡献体现在三个方面：1）引入一个专门针对基于 LiDAR 点云的动态场景的语义 SLAM 框架；2）利用语义信息和 Kalman 滤波，有效区分动态地标与半静态地标；3）在 SD-SLAM 过程中充分利用带有语义信息的半静态地标和纯静态地标，以提升定位与建图性能。为评估所提出的 SD-SLAM，使用被广泛采用的 KITTI 里程计数据集进行了测试。结果表明，所提出的 SD-SLAM 有效减轻了动态物体对 SLAM 的不利影响，提升了动态场景中的车辆定位与建图性能，同时构建了具有多种语义类别的静态语义地图，以增强环境理解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
