Towards High-Performance Solid-State-LiDAR-Inertial Odometry and Mapping
This work addresses the need for robust localization and mapping in robotics and autonomous systems, particularly for emerging solid-state LiDARs, though it appears incremental as it builds on existing LiDAR-inertial methods.
The authors tackled the problem of achieving high-performance odometry and mapping for both solid-state and mechanical LiDARs by developing a tightly-coupled LiDAR-inertial system, resulting in real-time capability and superior accuracy over state-of-the-art systems on public datasets and experiments.
We present a novel tightly-coupled LiDAR-inertial odometry and mapping scheme for both solid-state and mechanical LiDARs. As frontend, a feature-based lightweight LiDAR odometry provides fast motion estimates for adaptive keyframe selection. As backend, a hierarchical keyframe-based sliding window optimization is performed through marginalization for directly fusing IMU and LiDAR measurements. For the Livox Horizon, a newly released solid-state LiDAR, a novel feature extraction method is proposed to handle its irregular scan pattern during preprocessing. LiLi-OM (Livox LiDAR-inertial odometry and mapping) is real-time capable and achieves superior accuracy over state-of-the-art systems for both LiDAR types on public data sets of mechanical LiDARs and in experiments using the Livox Horizon. Source code and recorded experimental data sets are available at https://github.com/KIT-ISAS/lili-om.