融合多传感器提升四足机器人定位精度，尤其改善竖直方向漂移问题。
Multi-Sensor Fusion for Quadruped Robot State Estimation using Invariant Filtering and Smoothing
- 基于不变滤波与平滑框架，融合惯性、激光、里程计与GPS数据
- 室内误差降低28%，室外误差降低40%，显著优于LIO-SAM等方法
- 支持并行处理激光里程计，兼顾精度与计算效率，适合实际部署
本文提出两种基于不变扩展卡尔曼滤波（InEKF）与不变平滑器（IS）的多传感器状态估计框架，分别命名为E-InEKF与E-IS。通过引入满足群仿射性质的观测模型，将激光雷达里程计（LiDAR odometry）和全球定位系统（GPS）数据融合进滤波与平滑流程。激光里程计采用并行线程的ICP配准方式实现，保持了本体感知方法的计算效率。在KAIST HOUND2机器人上，针对室内外场景开展实验，对比了含与不含外部感知传感器的方案，并与基于激光的里程计方法（如LIO-SAM、FAST-LIO2）进行基准测试。结果表明，所提方法在相对位置误差（RPE）和绝对轨迹误差（ATE）方面均有显著改善，室内最高降低28%，室外最高降低40%。同时评估了E-InEKF与E-IS在精度与计算效率上的权衡。
原文摘要 · Abstract (English)
This letter introduces two multi-sensor state estimation frameworks for quadruped robots, built on the Invariant Extended Kalman Filter (InEKF) and Invariant Smoother (IS). The proposed methods, named E-InEKF and E-IS, fuse kinematics, IMU, LiDAR, and GPS data to mitigate position drift, particularly along the z-axis, a common issue in proprioceptive-based approaches. We derived observation models that satisfy group-affine properties to integrate LiDAR odometry and GPS into InEKF and IS. LiDAR odometry is incorporated using Iterative Closest Point (ICP) registration on a parallel thread, preserving the computational efficiency of proprioceptive-based state estimation. We evaluate E-InEKF and E-IS with and without exteroceptive sensors, benchmarking them against LiDAR-based odometry methods in indoor and outdoor experiments using the KAIST HOUND2 robot. Our methods achieve lower Relative Position Errors (RPE) and significantly reduce Absolute Trajectory Error (ATE), with improvements of up to 28% indoors and 40% outdoors compared to LIO-SAM and FAST-LIO2. Additionally, we compare E-InEKF and E-IS in terms of computational efficiency and accuracy.
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