Versatile LiDAR-Inertial Odometry With SE (2) Constraints for Ground Vehicles
Jiaying Chen, Han Wang, Minghui Hu, Ponnuthurai Nagaratnam Suganthan
TL;DR
This work tackles ground-vehicle SLAM by enforcing SE($2$) motion constraints while robustly handling out-of-$SE(2)$ perturbations. It presents SE2LIO, a tightly coupled LiDAR–IMU odometry framework that integrates IMU preintegration, feature-based LiDAR processing, two-stage motion distortion compensation, and SE(2)-XYZ constrained optimization to achieve real-time state estimation. Key contributions include a novel SE(2) constraints model that ingests non-planar perturbations, a two-stage deskewing workflow, and a tightly coupled fusion strategy that improves accuracy across indoor, outdoor, and KITTI datasets, with substantial RMSE reductions over state-of-the-art methods. The approach demonstrates the practical impact of combining planar motion priors with rich LiDAR features for robust, real-time ground-vehicle localization and mapping, while remaining computationally efficient for embedded platforms.
Abstract
LiDAR SLAM has become one of the major localization systems for ground vehicles since LiDAR Odometry And Mapping (LOAM). Many extension works on LOAM mainly leverage one specific constraint to improve the performance, e.g., information from on-board sensors such as loop closure and inertial state; prior conditions such as ground level and motion dynamics. In many robotic applications, these conditions are often known partially, hence a SLAM system can be a comprehensive problem due to the existence of numerous constraints. Therefore, we can achieve a better SLAM result by fusing them properly. In this paper, we propose a hybrid LiDAR-inertial SLAM framework that leverages both the on-board perception system and prior information such as motion dynamics to improve localization performance. In particular, we consider the case for ground vehicles, which are commonly used for autonomous driving and warehouse logistics. We present a computationally efficient LiDAR-inertial odometry method that directly parameterizes ground vehicle poses on SE(2). The out-of-SE(2) motion perturbations are not neglected but incorporated into an integrated noise term of a novel SE(2)-constraints model. For odometric measurement processing, we propose a versatile, tightly coupled LiDAR-inertial odometry to achieve better pose estimation than traditional LiDAR odometry. Thorough experiments are performed to evaluate our proposed method's performance in different scenarios, including localization for both indoor and outdoor environments. The proposed method achieves superior performance in accuracy and robustness.