固有受容感覚に基づく不変ロボット状態推定
Proprioceptive Invariant Robot State Estimation
IMUと関節キネマティクスのみを用いた不変カルマンフィルタによるデッドレコニング状態推定フレームワークDRIFTを開発し、脚・車輪・車両・船舶ロボットで実証した。
著者: Tzu-Yuan Lin, Tingjun Li, Wenzhe Tong, Maani Ghaffari
分類: cs.RO
原文アブストラクト
This paper reports on developing a real-time invariant proprioceptive robot state estimation framework called DRIFT. A didactic introduction to invariant Kalman filtering is provided to make this cutting-edge symmetry-preserving approach accessible to a broader range of robotics applications. Furthermore, this work dives into the development of a proprioceptive state estimation framework for dead reckoning that only consumes data from an onboard inertial measurement unit and kinematics of the robot, with two optional modules, a contact estimator and a gyro filter for low-cost robots, enabling a significant capability on a variety of robotics platforms to track the robot's state over long trajectories in the absence of perceptual data. Extensive real-world experiments using a legged robot, an indoor wheeled robot, a field robot, and a full-size vehicle, as well as simulation results with a marine robot, are provided to understand the limits of DRIFT.
