{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:18:37Z","timestamp":1760231917531,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:00:00Z","timestamp":1665446400000},"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>This paper considers the problem of determining the time-varying location of a nearly full hatch during cyclic transloading operations. Hatch location determination is a necessary step for automation of transloading, so that the crane can safely operate on the cargo in the hatch without colliding with the hatch edges. A novel approach is presented and evaluated by using data from a light detection and ranging (LiDAR) mounted on a pan-tilt unit (PT). Within each cycle, the hatch area is scanned, the data is processed, and the hatch corner locations are extracted. Computations complete less than 5 ms after the LiDAR scan completes, which is well within the time constraints imposed by the crane transloading cycle. Although the approach is designed to solve the challenging problem of a full hatch scenario, it also works when the hatch is not full, because in that case the hatch edges can be more easily distinguished from the cargo data. Therefore, the approach can be applied during the whole duration of either loading or unloading. Experimental results for hundreds of cycles are present to demonstrate the ability to track the hatch location as it moves and to assess the accuracy (standard deviation less than 0.30 m) and reliability (worst case error less than 0.35 m).<\/jats:p>","DOI":"10.3390\/rs14205069","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T06:13:27Z","timestamp":1665468807000},"page":"5069","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["LiDAR-Based Hatch Localization"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9515-4330","authenticated-orcid":false,"given":"Zeyi","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of California, Riverside, CA 92521, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1338-1805","authenticated-orcid":false,"given":"Xuqing","family":"Liu","sequence":"additional","affiliation":[{"name":"MicroStar Tech Co., Ltd., Santa Ana, CA 92705, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5461-6316","authenticated-orcid":false,"given":"Mike","family":"Ma","sequence":"additional","affiliation":[{"name":"MicroStar Tech Co., Ltd., Santa Ana, CA 92705, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6423-3423","authenticated-orcid":false,"given":"Guanlin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Data Science, The Chinese University of Hong Kong, Shenzhen 518172, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2077-8691","authenticated-orcid":false,"given":"Jay A.","family":"Farrell","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of California, Riverside, CA 92521, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,11]]},"reference":[{"key":"ref_1","first-page":"54","article-title":"A fast automated vision system for container corner casting recognition","volume":"24","author":"Mi","year":"2016","journal-title":"J. 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