ロボットハンドアイキャリブレーションのための次善視点選択
Next-Best-View Selection for Robot Eye-in-Hand Calibration
ロボットのハンドアイキャリブレーションにおいて、キャリブレーション精度を最大化するために次のロボット姿勢を能動的に選択する手法を提案し、シミュレーションと実機で有効性を示した。
著者: Jun Yang, Jason Rebello, Steven L. Waslander
分類: cs.RO
原文アブストラクト
Robotic eye-in-hand calibration is the task of determining the rigid 6-DoF pose of the camera with respect to the robot end-effector frame. In this paper, we formulate this task as a non-linear optimization problem and introduce an active vision approach to strategically select the robot pose for maximizing calibration accuracy. Specifically, given an initial collection of measurement sets, our system first computes the calibration parameters and estimates the parameter uncertainties. We then predict the next robot pose from which to collect the next measurement that brings about the maximum information gain (uncertainty reduction) in the calibration parameters. We test our approach on a simulated dataset and validate the results on a real 6-axis robot manipulator. The results demonstrate that our approach can achieve accurate calibrations using many fewer viewpoints than other commonly used baseline calibration methods.
