DL-SLAM: Direct 2.5D LiDAR SLAM for Autonomous Driving

Abstract

Precisely localizing a vehicle in the GNSS-denied urban area is crucial for autonomous driving. The occupancy grid-based 2D LiDAR SLAM methods scale poorly to outdoor road scenarios, while the 3D point cloud-based LiDAR SLAM methods suffer from huge computation and storage costs. Aiming at the precise real-time LiDAR SLAM for both indoor and outdoor, this paper proposed a direct 2.5D heightmap-based SLAM system. This system extended our previously proposed DLO (the direct 2.5D LiDAR odometry) method by introducing the 2.5D segment features for efficient loop closure detection. We experimented our SLAM method on the KITTI datasets and shown it superior performance compared with the existing LiDAR SLAM methods.