# CT-VoxelMap: Efficient Continuous-Time LiDAR-Inertial Odometry with Probabilistic Adaptive Voxel Mapping Source: [CT-VoxelMap: Efficient Continuous-Time LiDAR-Inertial Odometry with Probabilistic Adaptive Voxel Mapping]() Collection index: [llms.txt]() Data updated: 2026-04-08T05:00:04.658Z Authors: Lei Zhao, Xingyi Li, Tianchen Deng, Chuan Cao, Han Zhang, Weidong Chen Published: 2026-04-04T14:27:54.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Pose Estimation on MARS LVIG](): Pose Estimation; MARS-LVIG; Max Error: 3.183. - [Odometry on Botanical Dataset botanical01](): Odometry; Botanical Dataset botanical01; RMSE: 1.711. - [Odometry on Botanical Dataset (botanical02)](): Odometry; Botanical Dataset (botanical02); RMSE: 0.821. - [Odometry on Botanical Dataset (03)](): Odometry; Botanical Dataset (03); RMSE: 2.008. - [Pose Estimation on M2UD](): Pose Estimation; M2UD; Max Error: 1.435. - [Pose Estimation on MCD](): Pose Estimation; MCD; Max Error: 0.355. - [Pose Estimation on Diter++](): Pose Estimation; Diter++; Max Error: 0.466. ## Original sources and implementation [Original paper]() [PDF]()