SCE-LIO: An Enhanced LiDAR Inertial Odometry by Constructing Submap Constraints

In LiDAR-based Simultaneous Localization and Mapping (SLAM) systems, loop closure detection is crucial for enhancing the accuracy of odometry. However, constraints from loop closure detection are only provided when a loop is detected and can only enhance odometry accuracy at specific moments. Therefore, this letter proposes a LiDAR inertial odometry system that periodically provides submap constraints to the pose graph and enhances odometry accuracy through pose graph optimization. The system represents LiDAR keyframes as a collection of submaps containing overlapping information during the process of creating submap constraints. The optimal pose transformations between submaps, determined using the Iterative Closest Point (ICP) algorithm with point-to-line and point-to-plane methods, are recognized as submap constraints. During the backend optimization phase, submap constraints and adjacent LiDAR keyframe constraints are integrated into the pose graph. The pose graph is then optimized using the pose graph optimization method to achieve the optimal LiDAR pose estimation. Additionally, To further enhance pose estimation, point-to-plane correspondence is established by considering the differences in normal vectors of feature points between the scan and the map, a feature extraction method based on ground segmentation is proposed, and an integrated initial positioning module is created by incorporating preintegration and scan-to-scan matching. The results of simulation, public datasets and vehicle experiments show that the accuracy of the proposed algorithm is significantly improved compared to the advanced SLAM algorithm.