Learning Markerless Robot-Depth Camera Calibration and End-Effector Pose Estimation

• URL: http://arxiv.org/abs/2212.07567v1
• Date: Thu, 15 Dec 2022 00:53:42 GMT
• Title: Learning Markerless Robot-Depth Camera Calibration and End-Effector Pose Estimation
• Authors: Bugra C. Sefercik, Baris Akgun
• Abstract summary: We present a learning-based markerless extrinsic calibration system that uses a depth camera and does not rely on simulation data. We learn models for end-effector (EE) segmentation, single-frame rotation prediction and keypoint detection, from automatically generated real-world data. Our robustness with training data from multiple camera poses and test data from previously unseen poses give sub-centimeter evaluations and sub-deciradian average calibration and pose estimation errors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional approaches to extrinsic calibration use fiducial markers and learning-based approaches rely heavily on simulation data. In this work, we present a learning-based markerless extrinsic calibration system that uses a depth camera and does not rely on simulation data. We learn models for end-effector (EE) segmentation, single-frame rotation prediction and keypoint detection, from automatically generated real-world data. We use a transformation trick to get EE pose estimates from rotation predictions and a matching algorithm to get EE pose estimates from keypoint predictions. We further utilize the iterative closest point algorithm, multiple-frames, filtering and outlier detection to increase calibration robustness. Our evaluations with training data from multiple camera poses and test data from previously unseen poses give sub-centimeter and sub-deciradian average calibration and pose estimation errors. We also show that a carefully selected single training pose gives comparable results.

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