Cerberus: 敏捷な歩行のための低ドリフト視覚・慣性・脚オドメトリ
Cerberus: Low-Drift Visual-Inertial-Leg Odometry For Agile Locomotion
ステレオカメラ・IMU・関節エンコーダ・接触センサを用いて脚式ロボットの位置をリアルタイム推定し、運動学的パラメータのオンライン校正と接触外れ値除去によりドリフトを1%未満に抑えるオープンソースの状態推定手法を提案。
著者: Shuo Yang, Zixin Zhang, Zhengyu Fu, Zachary Manchester
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
We present an open-source Visual-Inertial-Leg Odometry (VILO) state estimation solution, Cerberus, for legged robots that estimates position precisely on various terrains in real time using a set of standard sensors, including stereo cameras, IMU, joint encoders, and contact sensors. In addition to estimating robot states, we also perform online kinematic parameter calibration and contact outlier rejection to substantially reduce position drift. Hardware experiments in various indoor and outdoor environments validate that calibrating kinematic parameters within the Cerberus can reduce estimation drift to lower than 1% during long distance high speed locomotion. Our drift results are better than any other state estimation method using the same set of sensors reported in the literature. Moreover, our state estimator performs well even when the robot is experiencing large impacts and camera occlusion. The implementation of the state estimator, along with the datasets used to compute our results, are available at https://github.com/ShuoYangRobotics/Cerberus.
