学習した接地イベントと不変カルマンフィルタによる脚式ロボットの状態推定
Legged Robot State Estimation using Invariant Kalman Filtering and Learned Contact Events
脚式ロボットの固有感覚データから接地イベントを学習で推定し、接触情報を活用した不変拡張カルマンフィルタで高精度なオドメトリを実現した研究。
著者: Tzu-Yuan Lin, Ray Zhang, Justin Yu, Maani Ghaffari
分類: cs.RO
原文アブストラクト
This work develops a learning-based contact estimator for legged robots that bypasses the need for physical sensors and takes multi-modal proprioceptive sensory data as input. Unlike vision-based state estimators, proprioceptive state estimators are agnostic to perceptually degraded situations such as dark or foggy scenes. While some robots are equipped with dedicated physical sensors to detect necessary contact data for state estimation, some robots do not have dedicated contact sensors, and the addition of such sensors is non-trivial without redesigning the hardware. The trained network can estimate contact events on different terrains. The experiments show that a contact-aided invariant extended Kalman filter can generate accurate odometry trajectories compared to a state-of-the-art visual SLAM system, enabling robust proprioceptive odometry.
