{"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/online-temporal-calibration-for-monocular","title":"Online Temporal Calibration for Monocular Visual-Inertial Systems","arxiv_id":"1808.00692","date":"2018-08-02","proceeding":null,"authors":["Tong Qin","Shaojie Shen"],"abstract":"Accurate state estimation is a fundamental module for various intelligent\napplications, such as robot navigation, autonomous driving, virtual and\naugmented reality. Visual and inertial fusion is a popular technology for 6-DOF\nstate estimation in recent years. Time instants at which different sensors'\nmeasurements are recorded are of crucial importance to the system's robustness\nand accuracy. In practice, timestamps of each sensor typically suffer from\ntriggering and transmission delays, leading to temporal misalignment (time\noffsets) among different sensors. Such temporal offset dramatically influences\nthe performance of sensor fusion. To this end, we propose an online approach\nfor calibrating temporal offset between visual and inertial measurements. Our\napproach achieves temporal offset calibration by jointly optimizing time\noffset, camera and IMU states, as well as feature locations in a SLAM system.\nFurthermore, the approach is a general model, which can be easily employed in\nseveral feature-based optimization frameworks. Simulation and experimental\nresults demonstrate the high accuracy of our calibration approach even compared\nwith other state-of-art offline tools. The VIO comparison against other methods\nproves that the online temporal calibration significantly benefits\nvisual-inertial systems. The source code of temporal calibration is integrated\ninto our public project, VINS-Mono.","url_abs":"http://arxiv.org/abs/1808.00692v1","url_pdf":"http://arxiv.org/pdf/1808.00692v1.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":"online-temporal-calibration-for-monocular","repo_url":"https://github.com/HKUST-Aerial-Robotics/VINS-Mono","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"robot-navigation","task_name":"Robot Navigation"},{"task_slug":"sensor-fusion","task_name":"Sensor Fusion"},{"task_slug":"state-estimation","task_name":"State Estimation"},{"task_slug":"time-offset-calibration","task_name":"Time Offset Calibration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1808.00692","atlas_url":"https://app.syntology.ai/?focus=1808.00692","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}