{"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/a-general-optimization-based-framework-for","title":"A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors","arxiv_id":"1901.03638","date":"2019-01-11","proceeding":null,"authors":["Tong Qin","Jie Pan","Shaozu Cao","Shaojie Shen"],"abstract":"Nowadays, more and more sensors are equipped on robots to increase robustness\nand autonomous ability. We have seen various sensor suites equipped on\ndifferent platforms, such as stereo cameras on ground vehicles, a monocular\ncamera with an IMU (Inertial Measurement Unit) on mobile phones, and stereo\ncameras with an IMU on aerial robots. Although many algorithms for state\nestimation have been proposed in the past, they are usually applied to a single\nsensor or a specific sensor suite. Few of them can be employed with multiple\nsensor choices. In this paper, we proposed a general optimization-based\nframework for odometry estimation, which supports multiple sensor sets. Every\nsensor is treated as a general factor in our framework. Factors which share\ncommon state variables are summed together to build the optimization problem.\nWe further demonstrate the generality with visual and inertial sensors, which\nform three sensor suites (stereo cameras, a monocular camera with an IMU, and\nstereo cameras with an IMU). We validate the performance of our system on\npublic datasets and through real-world experiments with multiple sensors.\nResults are compared against other state-of-the-art algorithms. We highlight\nthat our system is a general framework, which can easily fuse various sensors\nin a pose graph optimization. Our implementations are open\nsource\\footnote{https://github.com/HKUST-Aerial-Robotics/VINS-Fusion}.","url_abs":"http://arxiv.org/abs/1901.03638v1","url_pdf":"http://arxiv.org/pdf/1901.03638v1.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":"a-general-optimization-based-framework-for","repo_url":"https://github.com/HKUST-Aerial-Robotics/VINS-Fusion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-general-optimization-based-framework-for","repo_url":"https://github.com/hkust-aerial-robotics/vins-fisheye","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-general-optimization-based-framework-for","repo_url":"https://github.com/pjrambo/VINS-Fusion-gpu","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-general-optimization-based-framework-for","repo_url":"https://github.com/xuhao1/VINS-Fisheye","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"state-estimation","task_name":"State Estimation"},{"task_slug":"visual-odometry","task_name":"Visual Odometry"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.03638","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}