方向統計に基づく多様なセンサデータを用いた自己身体変形のオンライン推定
Online Estimation of Self-Body Deflection With Various Sensor Data Based on Directional Statistics
関節角と3次元姿勢をフォン・ミーゼス分布とビンガム分布で表現し、パーティクルフィルタで各種センサからロボットの姿勢をオンライン推定する手法を提案した論文。
著者: Hiroya Sato, Kento Kawaharazuka, Tasuku Makabe, Kei Okada, Masayuki Inaba
分類: cs.RO
原文アブストラクト
In this paper, we propose a method for online estimation of the robot's posture. Our method uses von Mises and Bingham distributions as probability distributions of joint angles and 3D orientation, which are used in directional statistics. We constructed a particle filter using these distributions and configured a system to estimate the robot's posture from various sensor information (e.g., joint encoders, IMU sensors, and cameras). Furthermore, unlike tangent space approximations, these distributions can handle global features and represent sensor characteristics as observation noises. As an application, we show that the yaw drift of a 6-axis IMU sensor can be represented probabilistically to prevent adverse effects on attitude estimation. For the estimation, we used an approximate model that assumes the actual robot posture can be reproduced by correcting the joint angles of a rigid body model. In the experiment part, we tested the estimator's effectiveness by examining that the joint angles generated with the approximate model can be estimated using the link pose of the same model. We then applied the estimator to the actual robot and confirmed that the gripper position could be estimated, thereby verifying the validity of the approximate model in our situation.
