Globally Consistent 3D LiDAR Mapping with GPU-accelerated GICP Matching Cost Factors

Abstract

This paper presents a real-time 3D LiDAR mapping framework based on global matching cost minimization. The proposed method constructs a factor graph that directly minimizes matching costs between frames over the entire map, unlike pose graph-based approaches that minimize errors in the pose space. For real-time global matching cost minimization, we use a voxel data association-based GICP matching cost factor that is able to fully leverage GPU parallel processing. The combination of the matching cost factor and GPU computation enables constraint of the relative pose between frames with a small overlap and creation of a densely connected factor graph. The mapping process is managed based on a voxel-based overlap metric that can quickly be evaluated on a GPU. We incorporate the proposed method with an external loop detection method in order to help the voxel-based matching cost factors to avoid convergence in a local solution. The experimental result on the KITTI dataset shows that the proposed approach improves the estimation accuracy of long trajectories.

IEEE Robotics and Automation Letters, Video: https://youtu.be/TarRKF_Xd2E

References in corpus (5)

• SuMa++: Efficient LiDAR-based Semantic SLAM
• OverlapNet: Loop Closing for LiDAR-based SLAM
• Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry
• Voxgraph: Globally Consistent, Volumetric Mapping using Signed Distance Function Submaps
• Efficient LiDAR Odometry for Autonomous Driving

Cited by in corpus (4)

• GLIM: 3D Range-Inertial Localization and Mapping with GPU-Accelerated Scan Matching Factors
• Tightly-Coupled LiDAR-IMU-Wheel Odometry with an Online Neural Kinematic Model Learning via Factor Graph Optimization
• Correcting Motion Distortion for LIDAR HD-Map Localization
• Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information