{"id":32933,"date":"2026-01-21T17:02:56","date_gmt":"2026-01-21T17:02:56","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/kiss-icp-in-defense-of-point-to-point-icp-simple-accurate-and-robust-registration-if-done-the-right-way\/"},"modified":"2026-09-17T11:26:57","modified_gmt":"2026-09-17T11:26:57","slug":"kiss-icp-in-defense-of-point-to-point-icp-simple-accurate-and-robust-registration-if-done-the-right-way","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/kiss-icp-in-defense-of-point-to-point-icp-simple-accurate-and-robust-registration-if-done-the-right-way\/","title":{"rendered":"KISS-ICP: In Defense of Point-to-Point ICP \u2013 Simple, Accurate, and Robust Registration If Done the Right Way"},"content":{"rendered":"<p>Robust and accurate pose estimation of a robotic platform, so-called sensor-based odometry, is an essential part of many robotic applications. While many sensor odometry systems made progress by adding more complexity to the ego-motion estimation process, we move in the opposite direction. By removing a majority of parts and focusing on the core elements, we obtain a surprisingly effective system that is simple to realize and can operate under various environmental conditions using different LiDAR sensors. Our odometry estimation approach relies on point-to-point ICP combined with adaptive thresholding for correspondence matching, a robust kernel, a simple but widely applicable motion compensation approach, and a point cloud subsampling strategy. This yields a system with only a few parameters that in most cases do not even have to be tuned to a specific LiDAR sensor. Our system performs on par with state-of-the-art methods under various operating conditions using different platforms using the same parameters: automotive platforms, UAV-based operation, vehicles like segways, or handheld LiDARs. We do not require integrating IMU data and solely rely on 3D point clouds obtained from a wide range of 3D LiDAR sensors, thus, enabling a broad spectrum of different applications and operating conditions. Our open-source system operates faster than the sensor frame rate in all presented datasets and is designed for real-world scenarios.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Robust and accurate pose estimation of a robotic platform, so-called sensor-based odometry, is an essential part of many robotic applications. While many sensor odometry systems made progress by adding more complexity to the ego-motion estimation process, we move in the opposite direction. By removing a majority of parts and focusing on the core elements, we obtain a surprisingly effective system that is simple to realize and can operate under various [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[30],"class_list":["post-32933","publication","type-publication","status-publish","hentry","publication-type-article"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32933","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/14"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32933\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32933"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32933"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}