脚式ロボットのためのマルチIMUセンサフュージョン
Multi-IMU Sensor Fusion for Legged Robots
脚式ロボットの複数リンクに搭載した低コストIMUと関節エンコーダ、カメラを拡張カルマンフィルタと因子グラフで融合し、着地衝撃や足滑りがあっても低ドリフトな姿勢・速度推定を実現した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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著者: Shuo Yang, Zixin Zhang, John Z. Zhang, Ibrahima Sory Sow, Zachary Manchester
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
