Globally Consistent and Tightly Coupled 3D LiDAR Inertial Mapping
This work addresses incremental improvements in localization and mapping for robotics and autonomous systems in feature-less or complex settings.
The paper tackles real-time 3D mapping by proposing a framework that minimizes global matching costs and tightly couples LiDAR and IMU data, achieving accurate and robust localization in challenging environments as shown on the Newer College and KAIST urban datasets.
This paper presents a real-time 3D mapping framework based on global matching cost minimization and LiDAR-IMU tight coupling. The proposed framework comprises a preprocessing module and three estimation modules: odometry estimation, local mapping, and global mapping, which are all based on the tight coupling of the GPU-accelerated voxelized GICP matching cost factor and the IMU preintegration factor. The odometry estimation module employs a keyframe-based fixed-lag smoothing approach for efficient and low-drift trajectory estimation, with a bounded computation cost. The global mapping module constructs a factor graph that minimizes the global registration error over the entire map with the support of IMU constraints, ensuring robust optimization in feature-less environments. The evaluation results on the Newer College dataset and KAIST urban dataset show that the proposed framework enables accurate and robust localization and mapping in challenging environments.