450 research outputs found
EasyHeC: Accurate and Automatic Hand-eye Calibration via Differentiable Rendering and Space Exploration
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 offers comprehensive coverage of positioning accuracy across the
entire robot configuration space. We introduce 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.Comment: Project page: https://ootts.github.io/easyhe
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