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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>IMLS-SLAM: scan-to-model matching based on 3D data · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1802.08633/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1802.08633/feed.xml" rel="self" type="application/rss+xml" /><item><title>IMLS-SLAM: scan-to-model matching based on 3D data — mentioned by dllu</title><link>https://news.ycombinator.com/item?id=19547102</link><guid isPermaLink="false">urn:hackerpapers:mention:1802.08633:19547102</guid><pubDate>Mon, 01 Apr 2019 19:20:58 GMT</pubDate><description>&lt;p&gt;Ouster SLAM works okay in a forest environment, such as driving with one Ouster OS-1 at highway speeds in Tahoe [0]. It is definitely much more challenging than an urban environment full of flat planes and right angles. Calibration, including range biases, is probably the one factor with the greatest impact on mapping quality. For example, range bias may cause curved walls, and beam angle biases may cause curved ground. I recall that the top scoring lidar SLAM algorithms on the KITTI data set all had to perform some calibration (for example, J. E. Deschaud found that all the beams on the…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1802.08633/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.RO</category></item></channel></rss>
