{"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-1","title":"A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors","arxiv_id":"1901.03642","date":"2019-01-11","proceeding":null,"authors":["Tong Qin","Shaozu Cao","Jie Pan","Shaojie Shen"],"abstract":"Accurate state estimation is a fundamental problem for autonomous robots. To\nachieve locally accurate and globally drift-free state estimation, multiple\nsensors with complementary properties are usually fused together. Local sensors\n(camera, IMU, LiDAR, etc) provide precise pose within a small region, while\nglobal sensors (GPS, magnetometer, barometer, etc) supply noisy but globally\ndrift-free localization in a large-scale environment. In this paper, we propose\na sensor fusion framework to fuse local states with global sensors, which\nachieves locally accurate and globally drift-free pose estimation. Local\nestimations, produced by existing VO/VIO approaches, are fused with global\nsensors in a pose graph optimization. Within the graph optimization, local\nestimations are aligned into a global coordinate. Meanwhile, the accumulated\ndrifts are eliminated. We evaluate the performance of our system on public\ndatasets and with real-world experiments. Results are compared against other\nstate-of-the-art algorithms. We highlight that our system is a general\nframework, which can easily fuse various global sensors in a unified pose graph\noptimization. Our implementations are open\nsource\\footnote{https://github.com/HKUST-Aerial-Robotics/VINS-Fusion}.","url_abs":"http://arxiv.org/abs/1901.03642v1","url_pdf":"http://arxiv.org/pdf/1901.03642v1.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-1","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-1","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-1","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-1","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":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"sensor-fusion","task_name":"Sensor Fusion"},{"task_slug":"state-estimation","task_name":"State Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.03642","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}