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It utilizes a feature extraction algorithm based on the evaluation of the curvature of points under test, to produce estimated smooth and non-smooth regions within typically laser based Point Cloud Data (PCD). This feature extractor (FE) however, does not take into account PCD spatial or detection uncertainty, which can result in the divergence of the LOAM algorithm. Therefore, this article proposes the use of the Curvature Scale Space (CSS) algorithm as a replacement for LOAM\u2019s current feature extractor. It justifies the substitution, based on the CSS algorithm\u2019s similar computational complexity but improved feature detection repeatability. LOAM\u2019s current feature extractor and the proposed CSS feature extractor are tested and compared with simulated and real data, including the KITTI odometry-laser data set. Additionally, a recent deep learning based LiDAR Odometry (LO) algorithm, the Convolutional Auto-Encoder (CAE)-LO algorithm, will also be compared, using this data set, in terms of its computational speed and performance. Performance comparisons are made based on the Absolute Trajectory Error (ATE) and Cardinalized Optimal Linear Assignment (COLA) metrics. Based on these metrics, the comparisons show significant improvements of the LOAM algorithm with the CSS feature extractor compared with the benchmark versions.<\/jats:p>","DOI":"10.1007\/s10846-024-02096-1","type":"journal-article","created":{"date-parts":[[2024,4,27]],"date-time":"2024-04-27T06:02:51Z","timestamp":1714197771000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Curvature Scale Space LiDAR Odometry And Mapping (LOAM)"],"prefix":"10.1007","volume":"110","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6777-4479","authenticated-orcid":false,"given":"Clayder","family":"Gonzalez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1085-0506","authenticated-orcid":false,"given":"Martin","family":"Adams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,27]]},"reference":[{"key":"2096_CR1","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1109\/MRA.2006.1678144","volume":"13","author":"T Bailey","year":"2006","unstructured":"Bailey, T., Durrant-Whyte, H.: Simultaneous localization and mapping (slam): Part i. 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