{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:46:12Z","timestamp":1784821572713,"version":"3.55.0"},"reference-count":17,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T00:00:00Z","timestamp":1680566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AI Hub Tampere"},{"name":"ERDF (EU Regional Development Funding)"},{"name":"Council of Tampere Region (Pirkanmaan liitto)"},{"name":"FIMA (Forum for Intelligent Machines)"},{"name":"Business Tampere"},{"name":"Business Finland"},{"name":"PEAMS (Platform Economy for Autonomous Mobile Machines Software Development)"},{"name":"Business Finland"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper studies the effect of reference frame selection in sensor-to-sensor extrinsic calibration when formulated as a motion-based hand\u2013eye calibration problem. As the sensor trajectories typically contain some composition of noise, the aim is to determine which selection strategies work best under which noise conditions. Different reference selection options are tested under varying noise conditions in simulations, and the findings are validated with real data from the KITTI dataset. The study is conducted for four state-of-the-art methods, as well as two proposed cost functions for nonlinear optimization. One of the proposed cost functions incorporates outlier rejection to improve calibration performance and was shown to significantly improve performance in the presence of outliers, and either match or outperform the other algorithms in other noise conditions. However, the performance gain from reference frame selection was deemed larger than that from algorithm selection. In addition, we show that with realistic noise, the reference frame selection method commonly used in the literature, is inferior to other tested options, and that relative error metrics are not reliable for telling which method achieves best calibration performance.<\/jats:p>","DOI":"10.3390\/s23073740","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T02:11:41Z","timestamp":1680660701000},"page":"3740","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Motion-Based Extrinsic Sensor-to-Sensor Calibration: Effect of Reference Frame Selection for New and Existing Methods"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6361-5892","authenticated-orcid":false,"given":"Tuomas","family":"V\u00e4lim\u00e4ki","sequence":"first","affiliation":[{"name":"Automation Technology and Mechanical Engineering, Tampere University, FI-33720 Tampere, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bharath","family":"Garigipati","sequence":"additional","affiliation":[{"name":"Automation Technology and Mechanical Engineering, Tampere University, FI-33720 Tampere, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6043-4236","authenticated-orcid":false,"given":"Reza","family":"Ghabcheloo","sequence":"additional","affiliation":[{"name":"Automation Technology and Mechanical Engineering, Tampere University, FI-33720 Tampere, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The KITTI dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. 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