{"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/rovo-robust-omnidirectional-visual-odometry","title":"ROVO: Robust Omnidirectional Visual Odometry for Wide-baseline Wide-FOV Camera Systems","arxiv_id":"1902.11154","date":"2019-02-28","proceeding":null,"authors":["Hochang Seok","Jongwoo Lim"],"abstract":"In this paper we propose a robust visual odometry system for a wide-baseline\ncamera rig with wide field-of-view (FOV) fisheye lenses, which provides full\nomnidirectional stereo observations of the environment. For more robust and\naccurate ego-motion estimation we adds three components to the standard VO\npipeline, 1) the hybrid projection model for improved feature matching, 2)\nmulti-view P3P RANSAC algorithm for pose estimation, and 3) online update of\nrig extrinsic parameters. The hybrid projection model combines the perspective\nand cylindrical projection to maximize the overlap between views and minimize\nthe image distortion that degrades feature matching performance. The multi-view\nP3P RANSAC algorithm extends the conventional P3P RANSAC to multi-view images\nso that all feature matches in all views are considered in the inlier counting\nfor robust pose estimation. Finally the online extrinsic calibration is\nseamlessly integrated in the backend optimization framework so that the changes\nin camera poses due to shocks or vibrations can be corrected automatically. The\nproposed system is extensively evaluated with synthetic datasets with\nground-truth and real sequences of highly dynamic environment, and its superior\nperformance is demonstrated.","url_abs":"http://arxiv.org/abs/1902.11154v2","url_pdf":"http://arxiv.org/pdf/1902.11154v2.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":"rovo-robust-omnidirectional-visual-odometry","repo_url":"https://github.com/renmengqisheng/stereo_multifisheye","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"motion-estimation","task_name":"Motion Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"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}