多IMU融合提升足式机器人定位精度，抗冲击与打滑
Multi-IMU Sensor Fusion for Legged Robots
- 用多个位置的惯性传感器修正本体里程计误差
- 实测在剧烈震动和打滑下位置偏差最小化
- 适合需要高精度定位的复杂地形足式机器人
本文提出一种足式机器人的状态估计方案，采用多组低成本、小型轻量化的惯性测量单元（IMU），在复杂运动条件下实现低漂移的姿态与速度估计。核心思路是利用机器人不同连杆上的多个IMU，纠正标准本体里程计中的主要误差源。通过扩展卡尔曼滤波器融合惯性传感器数据与关节编码器信息，并将滤波得到的速度估计结果与相机数据结合，构建基于因子图的滑动窗口估计算法，形成视觉-惯性-腿式里程计方法。通过理论分析和真实机器人在多种挑战性运动任务中采集的数据进行验证，算法在存在显著地面冲击、足部打滑及突然身体旋转的情况下，仍能保持极小的位置偏差。相关C++实现与大规模数据集已开源：https://github.com/ShuoYangRobotics/Cerberus2.0。
原文摘要 · Abstract (English)
This paper presents a state-estimation solution for legged robots that uses a set of low-cost, compact, and lightweight sensors to achieve low-drift pose and velocity estimation under challenging locomotion conditions. The key idea is to leverage multiple inertial measurement units on different links of the robot to correct a major error source in standard proprioceptive odometry. We fuse the inertial sensor information and joint encoder measurements in an extended Kalman filter, then combine the velocity estimate from this filter with camera data in a factor-graph-based sliding-window estimator to form a visual-inertial-leg odometry method. We validate our state estimator through comprehensive theoretical analysis and hardware experiments performed using real-world robot data collected during a variety of challenging locomotion tasks. Our algorithm consistently achieves minimal position deviation, even in scenarios involving substantial ground impact, foot slippage, and sudden body rotations. A C++ implementation, along with a large-scale dataset, is available at https://github.com/ShuoYangRobotics/Cerberus2.0.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。