滑りやすい環境における脚式ロボットの速度更新付き不変拡張カルマンフィルタによる状態推定
Legged Robot State Estimation in Slippery Environments Using Invariant Extended Kalman Filter with Velocity Update
滑りやすい路面を歩く脚式ロボット向けに、慣性・カメラ速度・脚運動学を融合する不変拡張カルマンフィルタを提案し、Cassie二足歩行ロボットで実証した。
著者: Sangli Teng, Mark Wilfried Mueller, Koushil Sreenath
分類: cs.RO
原文アブストラクト
This paper proposes a state estimator for legged robots operating in slippery environments. An Invariant Extended Kalman Filter (InEKF) is implemented to fuse inertial and velocity measurements from a tracking camera and leg kinematic constraints. {\color{black}The misalignment between the camera and the robot-frame is also modeled thus enabling auto-calibration of camera pose.} The leg kinematics based velocity measurement is formulated as a right-invariant observation. Nonlinear observability analysis shows that other than the rotation around the gravity vector and the absolute position, all states are observable except for some singular cases. Discrete observability analysis demonstrates that our filter is consistent with the underlying nonlinear system. An online noise parameter tuning method is developed to adapt to the highly time-varying camera measurement noise. The proposed method is experimentally validated on a Cassie bipedal robot walking over slippery terrain. A video for the experiment can be found at https://youtu.be/VIqJL0cUr7s.
