{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T17:47:53Z","timestamp":1762624073096,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,9,16]],"date-time":"2018-09-16T00:00:00Z","timestamp":1537056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["88887.123914\/2015-00"],"award-info":[{"award-number":["88887.123914\/2015-00"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["310264\/2015-4"],"award-info":[{"award-number":["310264\/2015-4"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We propose a versatile method for estimating the RMS error of depth data provided by generic 3D sensors with the capability of generating RGB and depth (D) data of the scene, i.e., the ones based on techniques such as structured light, time of flight and stereo. A common checkerboard is used, the corners are detected and two point clouds are created, one with the real coordinates of the pattern corners and one with the corner coordinates given by the device. After a registration of these two clouds, the RMS error is computed. Then, using curve fittings methods, an equation is obtained that generalizes the RMS error as a function of the distance between the sensor and the checkerboard pattern. The depth errors estimated by our method are compared to those estimated by state-of-the-art approaches, validating its accuracy and utility. This method can be used to rapidly estimate the quality of RGB-D sensors, facilitating robotics applications as SLAM and object recognition.<\/jats:p>","DOI":"10.3390\/s18093122","type":"journal-article","created":{"date-parts":[[2018,9,17]],"date-time":"2018-09-17T10:42:20Z","timestamp":1537180940000},"page":"3122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Versatile Method for Depth Data Error Estimation in RGB-D Sensors"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5550-392X","authenticated-orcid":false,"given":"Elizabeth V.","family":"Cabrera","sequence":"first","affiliation":[{"name":"Natalnet Associate Laboratories, Federal University of Rio Grande do Norte, Campus Universit\u00e1rio, Natal RN 59.078-970, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8578-4515","authenticated-orcid":false,"given":"Luis E.","family":"Ortiz","sequence":"additional","affiliation":[{"name":"Natalnet Associate Laboratories, Federal University of Rio Grande do Norte, Campus Universit\u00e1rio, Natal RN 59.078-970, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7780-7254","authenticated-orcid":false,"given":"Bruno M. F. da","family":"Silva","sequence":"additional","affiliation":[{"name":"Natalnet Associate Laboratories, Federal University of Rio Grande do Norte, Campus Universit\u00e1rio, Natal RN 59.078-970, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5650-1718","authenticated-orcid":false,"given":"Esteban W. G.","family":"Clua","sequence":"additional","affiliation":[{"name":"Institute of Computing, Fluminense Federal University, Campus Praia Vermelha, Niteroi RJ 24.310-346, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7735-5630","authenticated-orcid":false,"given":"Luiz M. G.","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Natalnet Associate Laboratories, Federal University of Rio Grande do Norte, Campus Universit\u00e1rio, Natal RN 59.078-970, Brazil"},{"name":"Institute of Computing, Fluminense Federal University, Campus Praia Vermelha, Niteroi RJ 24.310-346, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fankhauser, P., Bloesch, M., Rodriguez, D., Kaestner, R., Hutter, M., and Siegwart, R. (2015, January 27\u201331). Kinect v2 for mobile robot navigation: Evaluation and modeling. Proceedings of the 2015 International Conference on Advanced Robotics (ICAR), Istanbul, Turkey.","DOI":"10.1109\/ICAR.2015.7251485"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Menegatti, E., Michael, N., Berns, K., and Yamaguchi, H. (2016). A Comparison of 3D Sensors for Wheeled Mobile Robots. 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