DogLegs: 脚部搭載IMUを用いた脚ロボットのロバスト固有受容状態推定
DogLegs: Robust Proprioceptive State Estimation for Legged Robots Using Multiple Leg-Mounted IMUs
脚に複数のIMUを装着し、体幹IMU・関節エンコーダと拡張カルマンフィルタで融合することで、脚ロボットの状態推定精度を向上させる手法を提案した。
著者: Yibin Wu, Jian Kuang, Shahram Khorshidi, Xiaoji Niu, Lasse Klingbeil, Maren Bennewitz, Heiner Kuhlmann
分類: cs.RO
原文アブストラクト
Robust and accurate proprioceptive state estimation of the main body is crucial for legged robots to execute tasks in extreme environments where exteroceptive sensors, such as LiDARs and cameras, may become unreliable. In this paper, we propose DogLegs, a state estimation system for legged robots that fuses the measurements from a body-mounted inertial measurement unit (Body-IMU), joint encoders, and multiple leg-mounted IMUs (Leg-IMU) using an extended Kalman filter (EKF). The filter system contains the error states of all IMU frames. The Leg-IMUs are used to detect foot contact, thereby providing zero-velocity measurements to update the state of the Leg-IMU frames. Additionally, we compute the relative position constraints between the Body-IMU and Leg-IMUs by the leg kinematics and use them to update the main body state and reduce the error drift of the individual IMU frames. Field experimental results have shown that our proposed DogLegs system achieves better state estimation accuracy compared to the traditional leg odometry method (using only Body-IMU and joint encoders) across various terrains. We make our datasets publicly available to benefit the research community (https://github.com/YibinWu/leg-odometry).
