{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/probabilistic-rgb-d-odometry-based-on-points","title":"Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertainty","arxiv_id":"1706.04034","date":"2017-06-13","proceeding":null,"authors":["Pedro F. Proenca","Yang Gao"],"abstract":"This work proposes a robust visual odometry method for structured\nenvironments that combines point features with line and plane segments,\nextracted through an RGB-D camera. Noisy depth maps are processed by a\nprobabilistic depth fusion framework based on Mixtures of Gaussians to denoise\nand derive the depth uncertainty, which is then propagated throughout the\nvisual odometry pipeline. Probabilistic 3D plane and line fitting solutions are\nused to model the uncertainties of the feature parameters and pose is estimated\nby combining the three types of primitives based on their uncertainties.\nPerformance evaluation on RGB-D sequences collected in this work and two public\nRGB-D datasets: TUM and ICL-NUIM show the benefit of using the proposed depth\nfusion framework and combining the three feature-types, particularly in scenes\nwith low-textured surfaces, dynamic objects and missing depth measurements.","url_abs":"http://arxiv.org/abs/1706.04034v3","url_pdf":"http://arxiv.org/pdf/1706.04034v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"probabilistic-rgb-d-odometry-based-on-points","repo_url":"https://github.com/pedropro/OMG_Depth_Fusion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"visual-odometry","task_name":"Visual Odometry"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}