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

2026-04-04

AI Summary

This work addresses the instability and low accuracy of mobile robot localization under high-speed motion or on rough terrain by proposing a continuous-time, tightly coupled LiDAR-inertial odometry approach. The method employs B-spline trajectory parameterization over Lie groups to enable compact representation and simplified Jacobian computation. IMU preintegration is leveraged for online estimation of spline fitting errors, while a probabilistic adaptive voxel map and a feature re-estimation mechanism are introduced to balance computational efficiency and robustness. Comprehensive experiments on multiple challenging public datasets demonstrate that the proposed system consistently outperforms state-of-the-art methods, and ablation studies confirm the effectiveness and individual contributions of each module.

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.

Problem

• localization
• fast motion
• rough terrain
• onboard resources
• mobile robots

Innovation

• continuous-time odometry
• B-spline on Lie groups
• probabilistic voxel mapping
• IMU error propagation
• re-estimation policy