<!doctype html><html lang="en" class="no-js"><head><meta charset="utf-8"> <!-- begin SEO --><title>Rut depth detection for automated trafficability assessment - Ian Q. Mattson</title><meta name="description" content="The passing of a wheeled or tracked vehicle over soft or deformable soil creates ruts. The depth of these ruts is proportional to the weight of the vehicle and the soil trafficability; the ability of the soil to support traffic from vehicles. Assessing soil trafficability is often a manual and labor-intensive process. We evaluate the ability of lidar and depth cameras to detect changes in rut depth with the goal of minimizing manual or automated evaluation via soil strength testing. Our sensor-based approach mimics the process used by human operators when measuring rut depth. We compare this approach with machine-centered approaches with the goal of improving correlation between soil strength measurements and rut depth. In general, we find that all sensors are able to measure rut depth within the uncertainty bounds of soil and rut depth models for light vehicles."><meta property="article:published_time" content="2023-06-13T00:00:00+00:00"><link rel="canonical" href="https://iqmattso.github.io/publication/2023-06-13-rut-depth-detection-for-automated-trafficability-assessment.md"> <script type="application/ld+json"> { "@context" : "http://schema.org", "@type" : "Person", "name" : "Ian Q. Mattson", "url" : "https://iqmattso.github.io", "sameAs" : null } </script> <!-- end SEO --> <!-- Open Graph protocol data (https://ogp.me/), used by social media --><meta property="og:locale" content="en-US"><meta property="og:site_name" content="Ian Q. Mattson"><meta property="og:title" content="Rut depth detection for automated trafficability assessment"><meta property="og:type" content="article"><meta property="og:description" name="description" content="The passing of a wheeled or tracked vehicle over soft or deformable soil creates ruts. The depth of these ruts is proportional to the weight of the vehicle and the soil trafficability; the ability of the soil to support traffic from vehicles. Assessing soil trafficability is often a manual and labor-intensive process. We evaluate the ability of lidar and depth cameras to detect changes in rut depth with the goal of minimizing manual or automated evaluation via soil strength testing. Our sensor-based approach mimics the process used by human operators when measuring rut depth. We compare this approach with machine-centered approaches with the goal of improving correlation between soil strength measurements and rut depth. In general, we find that all sensors are able to measure rut depth within the uncertainty bounds of soil and rut depth models for light vehicles."><meta property="og:url" content="https://iqmattso.github.io/publication/2023-06-13-rut-depth-detection-for-automated-trafficability-assessment.md"> <!-- end Open Graph protocol --><link href="https://iqmattso.github.io/feed.xml" type="application/atom+xml" rel="alternate" title="Ian Q. 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Please <a href="http://browsehappy.com/">upgrade your browser</a> to improve your experience.</div><![endif]--><div class="masthead"><div class="masthead__inner-wrap"><div class="masthead__menu"><nav id="site-nav" class="greedy-nav"> <button><div class="navicon"></div></button><ul class="visible-links"><li class="masthead__menu-item masthead__menu-item--lg persist"><a href="https://iqmattso.github.io/">Ian Q. 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Mattson</h3><p class="author__pronouns">he/him</p><p class="author__bio">PhD Student at Michigan Tech</p></div><div class="author__urls-wrapper"> <button class="btn btn--inverse">Follow</button><ul class="author__urls social-icons"> <!-- Font Awesome icons / Biographic information --><li class="author__desktop"><i class="fas fa-fw fa-location-dot icon-pad-right" aria-hidden="true"></i>Houghton, MI</li><li class="author__desktop"><i class="fas fa-fw fa-building-columns icon-pad-right" aria-hidden="true"></i>Michigan Technological University</li><!-- Font Awesome and Academicons icons / Academic websites --><li><a href="https://scholar.google.com/citations?user=EfDws6IAAAAJ&hl"><i class="ai ai-google-scholar ai-fw icon-pad-right"></i>Google Scholar</a></li><li><a href="https://orcid.org/0009-0000-7573-6264"><i class="ai ai-orcid ai-fw icon-pad-right"></i>ORCID</a></li><!-- Font Awesome icons / Repositories and software development --><li><a href="https://github.com/iqmattso"><i class="fab fa-fw fa-github icon-pad-right" aria-hidden="true"></i>GitHub</a></li><!-- Font Awesome icons / Social media --><li><a href="https://www.linkedin.com/in/ian-mattson"><i class="fab fa-fw fa-linkedin icon-pad-right" aria-hidden="true"></i>LinkedIn</a></li></ul></div></div></div><article class="page" itemscope itemtype="http://schema.org/CreativeWork"><meta itemprop="headline" content="Rut depth detection for automated trafficability assessment"><meta itemprop="description" content="The passing of a wheeled or tracked vehicle over soft or deformable soil creates ruts. The depth of these ruts is proportional to the weight of the vehicle and the soil trafficability; the ability of the soil to support traffic from vehicles. Assessing soil trafficability is often a manual and labor-intensive process. We evaluate the ability of lidar and depth cameras to detect changes in rut depth with the goal of minimizing manual or automated evaluation via soil strength testing. Our sensor-based approach mimics the process used by human operators when measuring rut depth. We compare this approach with machine-centered approaches with the goal of improving correlation between soil strength measurements and rut depth. In general, we find that all sensors are able to measure rut depth within the uncertainty bounds of soil and rut depth models for light vehicles."><meta itemprop="datePublished" content="June 13, 2023"><div class="page__inner-wrap"><header><h1 class="page__title" itemprop="headline">Rut depth detection for automated trafficability assessment</h1><p>Published in <i>SPIE Defense + Commercial Sensing, Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023</i>, 2023</p></header><section class="page__content" itemprop="text"><p style="font-size: smaller">Recommended citation: I. Q. Mattson, Z. D. Jeffries, C. D. Majhor, and J. P. Bos, “Rut depth detection for automated trafficability assessment,” Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023, p. 26, Jun. 2023. doi:10.1117/12.2664429 <br /><a href="https://doi.org/10.1117/12.2664429">Download Paper</a></p></section><footer class="page__meta"></footer><section class="page__share"><h4 class="page__share-title">Share on</h4><a href="https://bsky.app/intent/compose?text=https://iqmattso.github.io/publication/2023-06-13-rut-depth-detection-for-automated-trafficability-assessment.md" class="btn btn--bluesky" title="Share on Bluesky"><i class="fab fa-bluesky" aria-hidden="true"></i><span> Bluesky</span></a> <a href="https://www.facebook.com/sharer/sharer.php?u=https://iqmattso.github.io/publication/2023-06-13-rut-depth-detection-for-automated-trafficability-assessment.md" class="btn btn--facebook" title="Share on Facebook"><i class="fab fa-facebook" aria-hidden="true"></i><span> Facebook</span></a> <a href="https://www.linkedin.com/shareArticle?mini=true&url=https://iqmattso.github.io/publication/2023-06-13-rut-depth-detection-for-automated-trafficability-assessment.md" class="btn btn--linkedin" title="Share on LinkedIn"><i class="fab fa-linkedin" aria-hidden="true"></i><span> LinkedIn</span></a> <a href="https://x.com/intent/post?text=https://iqmattso.github.io/publication/2023-06-13-rut-depth-detection-for-automated-trafficability-assessment.md" class="btn btn--x" title="Share on X"><i class="fab fa-x-twitter" aria-hidden="true"></i><span> X (formerly Twitter)</span></a></section><nav class="pagination"> <a href="https://iqmattso.github.io/publication/2022-06-06-reducing-ego-vehicle-energy-use-by-lane-level-positioning.md" class="pagination--pager" title="Reducing ego vehicle energy-use by LiDAR-based lane-level positioning ">Previous</a> <a href="https://iqmattso.github.io/publication/2025-09-18-red-vs-infrared-comparing-900nm-and-1550nm-lidar-performance-in-arctic-winter-conditions.md" class="pagination--pager" title="Red vs. Infrared: comparing 900nm and 1550nm Lidar performance in arctic winter conditions ">Next</a></nav></div></article></div><div class="page__footer"><footer> <!-- start custom footer snippets --> <a href="/sitemap/">Sitemap</a> <!-- Support for MatJax --> <script defer src="https://cdnjs.cloudflare.com/polyfill/v3/polyfill.min.js?features=es6"></script> <script defer src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js" id="MathJax-script"></script> <!-- Support for Plotly --> <script defer src='https://cdnjs.cloudflare.com/ajax/libs/plotly.js/3.0.1/plotly.min.js'></script> <!-- Support for Mermaid --> <script type="module"> import mermaid from 'https://cdn.jsdelivr.net/npm/mermaid@11/dist/mermaid.esm.min.mjs'; mermaid.initialize({startOnLoad:true, theme:'default'}); await mermaid.run({querySelector:'code.language-mermaid'}); </script> <!-- end custom footer snippets --><div class="page__footer-follow"><ul class="social-icons"><li><strong>Follow:</strong></li><li><a href="https://github.com/iqmattso"><i class="fab fa-github" aria-hidden="true"></i> GitHub</a></li><li><a href="https://iqmattso.github.io/feed.xml"><i class="fa fa-fw fa-rss-square" aria-hidden="true"></i> Feed</a></li></ul></div><div class="page__footer-copyright"> &copy; 2025 Ian Q. 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