SD-SLAM: A Semantic SLAM Approach for Dynamic Scenes Based on LiDAR Point Clouds

TL;DR

SD-SLAM is a novel semantic SLAM approach that leverages LiDAR point clouds, semantics, and Kalman filtering to improve localization and mapping in dynamic scenes for autonomous vehicles.

Contribution

This work introduces a semantic SLAM framework specifically designed for dynamic scenes, utilizing semantics and Kalman filtering to distinguish dynamic from static landmarks.

Findings

Improves localization accuracy in dynamic environments

Constructs detailed static semantic maps with multiple classes

Mitigates effects of dynamic objects on SLAM performance

Abstract

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.