用递归样条法实现轻量级实时激光里程计，支持多传感器融合。
RESPLE: Recursive Spline Estimation for LiDAR-Based Odometry
- 基于B样条构建递归贝叶斯估计框架，直接优化位姿控制点。
- 在真实场景中实现与主流方法相当甚至更优的精度和实时性。
- 适用于多激光雷达+惯导组合，特别适合高动态复杂环境。
我们提出一种基于B样条的新型递归贝叶斯估计框架，用于连续时间下的6自由度动态运动估计。状态向量由一组递归的位置控制点和方向增量控制点构成，通过改进的迭代扩展卡尔曼滤波实现高效估计，无需误差状态表示。由此得到的递归样条估计算法（RESPLE）进一步发展为一套通用的直接激光雷达里程计解决方案，支持单或多激光雷达与惯性测量单元（IMU）融合。我们在公开数据集及自采实验中进行了广泛评估，涵盖多种传感器配置、平台和环境。相比现有系统，RESPLE在保持实时性的同时，实现了相当或更优的估计精度与鲁棒性。结果表明，RESPLE在处理高动态运动和复杂场景方面表现优异，具备轻量化与灵活性，展现出作为多传感器运动估计通用框架的强大潜力。源代码与实验数据集已开源：https://github.com/ASIG-X/RESPLE。
原文摘要 · Abstract (English)
We present a novel recursive Bayesian estimation framework using B-splines for continuous-time 6-DoF dynamic motion estimation. The state vector consists of a recurrent set of position control points and orientation control point increments, enabling efficient estimation via a modified iterated extended Kalman filter without involving error-state formulations. The resulting recursive spline estimator (RESPLE) is further leveraged to develop a versatile suite of direct LiDAR-based odometry solutions, supporting the integration of one or multiple LiDARs and an IMU. We conduct extensive real-world evaluations using public datasets and our own experiments, covering diverse sensor setups, platforms, and environments. Compared to existing systems, RESPLE achieves comparable or superior estimation accuracy and robustness, while attaining real-time efficiency. Our results and analysis demonstrate RESPLE's strength in handling highly dynamic motions and complex scenes within a lightweight and flexible design, showing strong potential as a universal framework for multi-sensor motion estimation. We release the source code and experimental datasets at https://github.com/ASIG-X/RESPLE .
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