{"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/brain-inspired-visual-odometry-balancing","title":"Brain-Inspired Visual Odometry: Balancing Speed and Interpretability through a System of Systems Approach","arxiv_id":"2312.13162","date":"2023-12-20","proceeding":null,"authors":["Habib Boloorchi Tabrizi","Christopher Crick"],"abstract":"In this study, we address the critical challenge of balancing speed and accuracy while maintaining interpretablity in visual odometry (VO) systems, a pivotal aspect in the field of autonomous navigation and robotics. Traditional VO systems often face a trade-off between computational speed and the precision of pose estimation. To tackle this issue, we introduce an innovative system that synergistically combines traditional VO methods with a specifically tailored fully connected network (FCN). Our system is unique in its approach to handle each degree of freedom independently within the FCN, placing a strong emphasis on causal inference to enhance interpretability. This allows for a detailed and accurate assessment of relative pose error (RPE) across various degrees of freedom, providing a more comprehensive understanding of parameter variations and movement dynamics in different environments. Notably, our system demonstrates a remarkable improvement in processing speed without compromising accuracy. In certain scenarios, it achieves up to a 5% reduction in Root Mean Square Error (RMSE), showcasing its ability to effectively bridge the gap between speed and accuracy that has long been a limitation in VO research. This advancement represents a significant step forward in developing more efficient and reliable VO systems, with wide-ranging applications in real-time navigation and robotic systems.","url_abs":"https://arxiv.org/abs/2312.13162v1","url_pdf":"https://arxiv.org/pdf/2312.13162v1.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":"brain-inspired-visual-odometry-balancing","repo_url":"https://github.com/habib-Boloorchi/CIVO-Visual-Odometry-","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"autonomous-navigation","task_name":"Autonomous Navigation"},{"task_slug":"causal-inference","task_name":"Causal Inference"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"visual-odometry","task_name":"Visual Odometry"}],"methods":[{"method_slug":"causal-inference","method_name":"Causal inference"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fcn","method_name":"FCN"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-odometry-on-euroc-mav","task":"Visual Odometry","dataset":"EuRoC MAV","model":"CIVO","rank_in_archive_order":1,"of":1,"metrics":{"Relative Position Error Translation [cm]":"1.3574"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}