CT-VoxelMap：高效的连续时间激光雷达-惯性里程计与概率自适应体素映射
CT-VoxelMap: Efficient Continuous-Time LiDAR-Inertial Odometry with Probabilistic Adaptive Voxel Mapping
AI总结：
本文提出CT-VoxelMap，通过概率自适应体素映射提升移动机器人在快速运动或粗糙地形中的定位稳定性与准确性，采用B-样条基方法优化连续时间表示，改进雅可比推导并增强系统鲁棒性。
摘要：
在快速运动或粗糙地形中维持稳定的定位仍然对移动机器人具有挑战。目前，基于连续时间表示的多传感器融合方法提供了一种潜在有效的解决方案。其中，基于样条的方法为连续时间表示提供了高效直观的途径。先前基于B-样条的连续时间里程计方法要么将控制点视为需估计的变量，要么在四元数空间中进行估计，这导致推导分析雅可比时复杂且常忽略样条与真实轨迹之间的时间拟合误差。为解决这些问题，我们首先提出将控制点增量在矩阵李群上表示为需估计的变量。利用B-样条的累积形式特性，我们推导出更紧凑的表达式，从而得到更简单的分析雅可比，而无需额外的边界条件考虑。其次，我们利用IMU测量的前向传播信息在线估计拟合误差，并进一步引入混合特征基于的体素地图管理策略，提高系统精度和鲁棒性。最后，我们提出一种重新估计策略，显著提高系统计算效率和鲁棒性。所提方法在多个具有挑战性的公开数据集上进行了评估，证明其在大多数序列中表现优越。进行了详细的消融研究以分析每个模块对整体姿态估计系统的影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
