CT-VoxelMap: Efficient Continuous-Time LiDAR-Inertial Odometry with Probabilistic Adaptive Voxel Mapping

TL;DR

This paper introduces CT-VoxelMap, a continuous-time LiDAR-inertial odometry method that improves accuracy and efficiency through novel control point representation, online fitting error estimation, and hybrid voxel mapping, validated on public datasets.

Contribution

It proposes a new control point representation on matrix Lie groups, a hybrid voxel map management strategy, and a re-estimation policy for enhanced accuracy and computational efficiency.

Findings

Outperforms existing methods on multiple public datasets.

Simplifies Jacobian derivation with a compact formulation.

Achieves real-time performance with improved robustness.

Abstract

Maintaining stable and accurate localization during fast motion or on rough terrain remains highly challenging for mobile robots with onboard resources. Currently, multi-sensor fusion methods based on continuous-time representation offer a potential and effective solution to this challenge. Among these, spline-based methods provide an efficient and intuitive approach for continuous-time representation. Previous continuous-time odometry works based on B-splines either treat control points as variables to be estimated or perform estimation in quaternion space, which introduces complexity in deriving analytical Jacobians and often overlooks the fitting error between the spline and the true trajectory over time. To address these issues, we first propose representing the increments of control points on matrix Lie groups as variables to be estimated. Leveraging the feature of the cumulative form of B-splines, we derive a more compact formulation that yields simpler analytical Jacobians without requiring additional boundary condition considerations. Second, we utilize forward propagation information from IMU measurements to estimate fitting errors online and further introduce a hybrid feature-based voxel map management strategy, enhancing system accuracy and robustness. Finally, we propose a re-estimation policy that significantly improves system computational efficiency and robustness. The proposed method is evaluated on multiple challenging public datasets, demonstrating superior performance on most sequences. Detailed ablation studies are conducted to analyze the impact of each module on the overall pose estimation system.