{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T06:04:04Z","timestamp":1768802644701,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2015,8,17]],"date-time":"2015-08-17T00:00:00Z","timestamp":1439769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Velodyne LiDAR series is one of the most popular spinning beam LiDAR systems currently available on the market. In this paper, the temporal stability of the range measurements of the Velodyne HDL-32E LiDAR system is first investigated as motivation for the development of a new automatic calibration method that allows quick and frequent recovery of the inherent time-varying errors. The basic principle of the method is that the LiDAR\u2019s internal systematic error parameters are estimated by constraining point clouds of some known and automatically detected cylindrical features such as lamp poles to fit to the 3D cylinder models. This is analogous to the plumb-line calibration method in which the lens distortion parameters are estimated by constraining the image points of straight lines to fit to the 2D line model. The calibration can be performed at every measurement epoch in both static and kinematic modes. Four real datasets were used to verify the method, two of which were captured in static mode and the other two in kinematic mode. The overall results indicate that up to approximately 72% and 41% accuracy improvement were realized as a result of the calibration for the static and kinematic datasets, respectively.<\/jats:p>","DOI":"10.3390\/rs70810480","type":"journal-article","created":{"date-parts":[[2015,8,18]],"date-time":"2015-08-18T05:43:13Z","timestamp":1439876593000},"page":"10480-10500","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Automatic In Situ Calibration of a Spinning Beam LiDAR System in Static and Kinematic Modes"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3318-7540","authenticated-orcid":false,"given":"Ting","family":"Chan","sequence":"first","affiliation":[{"name":"Department of Geomatics Engineering, University of Calgary, 2500 University Dr NW, Calgary,  AB T2N 1N4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Derek","family":"Lichti","sequence":"additional","affiliation":[{"name":"Department of Geomatics Engineering, University of Calgary, 2500 University Dr NW, Calgary,  AB T2N 1N4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,8,17]]},"reference":[{"key":"ref_1","unstructured":"Pedersen, L., Allan, M., Utz, H., Deans, M., Bouyssounouse, X., Choi, Y., Fl\u00fcckiger, L., Lee, S.Y., To, V., and Loh, J. 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