Invariant-DLIO: Direct LiDAR-Inertial Odometry Based on Invariant Kalman Filtering

The topic of light detection and ranging (LiDAR)–inertial odometry (LIO) is raising the interest of researchers as one of the key areas for robotic navigation, among which extended Kalman filtering (EKF)-based LIO has become the mainstream of LIO because of its excellent computational speed and good accuracy. However, the EKF-based methods cannot avoid the inconsistency from estimation error linearization. As a complement, invariant EKF (InEKF) designed for state trajectories lying on the matrix Lie groups has been proposed and proved to be excellent in convergence and consistency. In this article, we propose the method of direct LIO based on invariant Kalman filtering (Invariant-DLIO), which contains the InEKF-based state estimator with the fusion of IMU measurements and LiDAR point clouds, where the error dynamics meet the properties of the log-linear and trajectory-independent. A lightweight scan-to-mapping module is also designed for the refinement of pose estimation, where the mapping is updated and operated with O(1) time complexity. Extensive experiments, including different public datasets and Magni robot data acquisition, are conducted in comparison with a series of state-of-the-art LIO/LO methods. Experimental results show that Invariant-DLIO achieves superior accuracy and efficiency.