Mergeable Probabilistic Voxel Mapping for LiDAR–Inertial–Visual Odometry

To address the limitations of existing LiDAR–visual fusion methods in adequately accounting for map uncertainties induced by LiDAR measurement noise, this paper introduces a LiDAR–inertial–visual odometry framework leveraging mergeable probabilistic voxel mapping. The method innovatively employs probabilistic voxel models to characterize uncertainties in environmental geometric plane features and optimizes computational efficiency through a voxel merging strategy. Additionally, it integrates color information from cameras to further enhance localization accuracy. Specifically, in the LiDAR–inertial odometry (LIO) subsystem, a probabilistic voxel plane model is constructed for LiDAR point clouds to explicitly represent measurement noise uncertainty, thereby improving the accuracy and robustness of point cloud registration. A voxel merging strategy based on the union-find algorithm is introduced to merge coplanar voxel planes, reducing computational load. In the visual–inertial odometry (VIO) subsystem, image tracking points are generated through a global map projection, and outlier points are eliminated using a random sample consensus algorithm based on a dynamic Bayesian network. Finally, state estimation accuracy is enhanced by jointly optimizing frame-to-frame reprojection errors and frame-to-map RGB color errors. Experimental results demonstrate that the proposed method achieves root mean square errors (RMSEs) of absolute trajectory error at 0.478 m and 0.185 m on the M2DGR and NTU-VIRAL datasets, respectively, while attaining real-time performance with an average processing time of 39.19 ms per-frame on the NTU-VIRAL datasets. Compared to state-of-the-art approaches, our method exhibits significant improvements in both accuracy and computational efficiency.