{"id":32246,"date":"2026-01-21T17:01:33","date_gmt":"2026-01-21T17:01:33","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/revisiting-fast-and-accurate-rgb-d-odometry-for-real-world-use-by-embracing-simplicity\/"},"modified":"2026-06-08T13:18:47","modified_gmt":"2026-06-08T13:18:47","slug":"revisiting-fast-and-accurate-rgb-d-odometry-for-real-world-use-by-embracing-simplicity","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/revisiting-fast-and-accurate-rgb-d-odometry-for-real-world-use-by-embracing-simplicity\/","title":{"rendered":"Revisiting Fast and Accurate {RGB}-D Odometry for Real-World Use by Embracing Simplicity"},"content":{"rendered":"<p>Robust and accurate pose estimation of a robot is essential for many tasks. Over the past decades, researchers have explored odometry estimation using various sensors, including {RGB} cameras, {LiDARs}, and {RGB}-D cameras. {RGB}-D cameras, mainly, are attractive sensors as they provide color and depth. Despite the progress made in the {RGB}-D odometry approaches and systems sharing code, their practical use is often non-trivial due to technical debt in several open-source implementations. In some cases, approaches have been tuned to specific benchmarks, complicating the extension of implementations to different {RGB}-D sensors and environments. This paper introduces an easy-to-use and easy-to-understand yet robust {RGB}-D odometry pipeline adaptable across sensor platforms. We focus on simplicity and contribute a non-black-box {RGB}-D odometry approach and proper implementation to the community. Our open-source system utilizes {ORB} features for correspondence estimation and employs a frame-to-map registration strategy to estimate camera poses. Our system achieves performance on par with state-of-the-art methods while running faster than the sensor frame rate on a single-core {CPU}. Importantly, our approach does not require integrating {IMU} data or additional hardware like {GPUs}, making it easily deployable on various platforms.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Robust and accurate pose estimation of a robot is essential for many tasks. Over the past decades, researchers have explored odometry estimation using various sensors, including {RGB} cameras, {LiDARs}, and {RGB}-D cameras. {RGB}-D cameras, mainly, are attractive sensors as they provide color and depth. Despite the progress made in the {RGB}-D odometry approaches and systems sharing code, their practical use is often non-trivial due to technical debt in several open-source [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-32246","publication","type-publication","status-publish","hentry","publication-type-inproceedings"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32246","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\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32246\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32246"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32246"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}