A High-Precision LiDAR-Inertial Odometry via Invariant Extended Kalman Filtering and Efficient Surfel Mapping
Houzhan Zhang, Rong Xiao, J. Q. Li, Chuangye Yan, Huajin Tang
Abstract
Simultaneous localization and mapping (SLAM) via light detection and ranging (LiDAR)-inertial odometry is a crucial technology in many automated applications. However, constructing a consistent state estimator with an efficient mapping method still remains a challenge for LiDAR-inertial odometry (LIO) systems. In this article, we propose a tightly coupled LIO system via invariant extended Kalman filter (InEKF) and efficient surfel mapping. First, based on the InEKF theory, we build a consistent state estimator for a tightly coupled LIO system. Second, we propose a novel LIO system by combining the InEKF state estimator with a surfel-based map, named SuIn-LIO, which not only enables the accuracy of state estimation and mapping but also enables real-time registration of a new LiDAR scan. Extensive experiments on different public benchmark datasets demonstrate that SuIn-LIO can achieve comparable performance with other state-of-the-art methods in accuracy and efficiency. To benefit of the community, our implementation will be open-sourced on Github.