标题： 多IMU传感器融合用于腿足机器人 显示英文标题

标题： Multi-IMU Sensor Fusion for Legged Robots

摘要： 本文提出了一种用于腿式机器人的状态估计解决方案，该方案使用一组低成本、紧凑且轻量的传感器，在具有挑战性的运动条件下实现低漂移的姿态和速度估计。 其核心思想是利用机器人不同部位上的多个惯性测量单元来校正标准本体感觉里程计中的主要误差源。 我们将惯性传感器信息与关节编码器测量数据融合在一个扩展卡尔曼滤波器中，然后将该滤波器的速度估计结果与相机数据结合，通过基于因子图的滑动窗口估计器形成视觉-惯性-腿里程计方法。 我们通过全面的理论分析和在多种具有挑战性的运动任务中收集的真实机器人数据进行的硬件实验来验证我们的状态估计器。 即使在涉及大量地面冲击、脚部打滑和突然身体旋转的场景中，我们的算法也能持续实现最小的位置偏差。 可在 https://github.com/ShuoYangRobotics/Cerberus2.0 获取C++实现和一个大规模数据集。 显示英文摘要

摘要： 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.