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Most approaches to hand-eye calibration rely on external markers or human assistance. We proposed a novel methodology that addresses the hand-eye calibration problem using the robot base as a reference, eliminating the need for external calibration objects or human intervention. Using point clouds of the robot base, a transformation matrix from the coordinate frame of the camera to the robot base is established as \u201c<jats:bold>I<\/jats:bold>=<jats:bold>AXB<\/jats:bold>.\u201d To this end, we exploit learning-based 3D detection and registration algorithms to estimate the location and orientation of the robot base. The robustness and accuracy of the method are quantified by ground-truth-based evaluation, and the accuracy result is compared with other 3D vision-based calibration methods. To assess the feasibility of our methodology, we carried out experiments utilizing a low-cost structured light scanner across varying joint configurations and groups of experiments. The proposed hand-eye calibration method achieved a translation deviation of 0.930 mm and a rotation deviation of 0.265 degrees according to the experimental results. Additionally, the 3D reconstruction experiments demonstrated a rotation error of 0.994 degrees and a position error of 1.697 mm. Moreover, our method offers the potential to be completed in 1 second, which is the fastest compared to other 3D hand-eye calibration methods. We conduct indoor 3D reconstruction and robotic grasping experiments based on our hand-eye calibration method. Related code is released at<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/leihui6\/LRBO\">https:\/\/github.com\/leihui6\/LRBO<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s10846-024-02166-4","type":"journal-article","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T12:04:29Z","timestamp":1725537869000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Automatic Robot Hand-Eye Calibration Enabled by Learning-Based 3D Vision"],"prefix":"10.1007","volume":"110","author":[{"given":"Leihui","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingyu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Riwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuping","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,5]]},"reference":[{"key":"2166_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2023.102564","volume":"83","author":"X Yang","year":"2023","unstructured":"Yang, X., Zhou, Z., S\u00f8rensen, J.H., Christensen, C.B., \u00dcnalan, M., Zhang, X.: Automation of sme production with a cobot system powered by learning-based vision. 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