CPBA-LIWO: Continuous-Time LiDAR-Inertial-Wheel Odometry Based on Probabilistic Bundle Adjustment

Abstract

LiDAR-based odometry is widely used in ground robot localization. However, current methods encounter challenges in accuracy and robustness due to structural degradation, system observational error, and accumulated error. To address the above issues, we propose CPBA-LIWO, a continuous-time LiDAR-Inertial-Wheel (LIW) odometry based on probabilistic bundle adjustment (PBA) within a sliding window. This method constructs a general wheel model, which is used for the complementary fusion of LiDAR, IMU and wheel data through a continuous-time trajectory using a B-spline curve, thereby improving the robustness of the system in structurally degraded environments. Furthermore, to improve the accuracy of long-distance odometry, we propose a probabilistic model for the voxel plane and implement a sliding-window voxel PBA backend based on this model. The experimental results on the M2DGR-plus and KAIST datasets demonstrate that our method outperforms state-of-the-art LiDAR-based odometry in terms of accuracy and robustness.