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Reconstruction-Based Hand-Eye Calibration Using Arbitrary Objects
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AI
DOI：10.1109/TII.2022.3203771.png)
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Abstract
En 中文
This article introduces a flexible hand-eye calibration technique for a 3-D sensor from a reconstruction perspective, with no need for a specialized and accurate calibration rig. Our intention is to find the hand-eye relation that simultaneously aligns multiview point clouds of a common scene into the robot base frame, namely simultaneous calibration and reconstruction. To achieve this goal, a novel variant of iterative closest point (ICP) algorithm based on the Gauss-Newton method and Lie algebra is proposed, which iteratively transforms multiview point clouds into the robot base frame, estimates point-to-point correspondences between point clouds then refines the hand-eye relation to minimize the Euclidean distance between corresponding points. In addition to the calibration result, it returns a preliminary reconstruction as a byproduct. Cases of degeneracy and applicable conditions are given and proved. Using arbitrary daily objects with no prior information and a real robotic eye-in-hand system, we verify our method feasible and effective.
Keywords:
Robot sensing systems
Calibration
Robots
Point cloud compression
Three-dimensional displays
Service robots
Robot motion
3-D reconstruction
arbitrary objects
computer vision and its industrial application
hand-eye calibration
iterative closest point (ICP) variant
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