EasyHeC: 微分可能レンダリングと空間探索による高精度で自動的なハンドアイキャリブレーション
EasyHeC: Accurate and Automatic Hand-eye Calibration via Differentiable Rendering and Space Exploration
微分可能レンダリングと一貫性に基づく関節空間探索を組み合わせ、マーカー不要で高精度なハンドアイキャリブレーションを自動で行う手法を提案した。
著者: Linghao Chen, Yuzhe Qin, Xiaowei Zhou, Hao Su
分類: cs.RO, cs.CV
原文アブストラクト
Hand-eye calibration is a critical task in robotics, as it directly affects the efficacy of critical operations such as manipulation and grasping. Traditional methods for achieving this objective necessitate the careful design of joint poses and the use of specialized calibration markers, while most recent learning-based approaches using solely pose regression are limited in their abilities to diagnose inaccuracies. In this work, we introduce a new approach to hand-eye calibration called EasyHeC, which is markerless, white-box, and delivers superior accuracy and robustness. We propose to use two key technologies: differentiable rendering-based camera pose optimization and consistency-based joint space exploration, which enables accurate end-to-end optimization of the calibration process and eliminates the need for the laborious manual design of robot joint poses. Our evaluation demonstrates superior performance in synthetic and real-world datasets, enhancing downstream manipulation tasks by providing precise camera poses for locating and interacting with objects. The code is available at the project page: https://ootts.github.io/easyhec.
