{"ID":2847802,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00306","arxiv_id":"2511.00306","title":"FGO MythBusters: Explaining how Kalman Filter variants achieve the same performance as FGO in navigation applications","abstract":"Sliding window-factor graph optimization (SW-FGO) has gained more and more attention in navigation research due to its robust approximation to non-Gaussian noises and nonlinearity of measuring models. There are lots of works focusing on its application performance compared to extended Kalman filter (EKF) but there is still a myth at the theoretical relationship between the SW-FGO and EKF. In this paper, we find the necessarily fair condition to connect SW-FGO and Kalman filter variants (KFV) (e.g., EKF, iterative EKF (IEKF), robust EKF (REKF) and robust iterative EKF (RIEKF)). Based on the conditions, we propose a recursive FGO (Re-FGO) framework to represent KFV under SW-FGO formulation. Under explicit conditions (Markov assumption, Gaussian noise with L2 loss, and a one-state window), Re-FGO regenerates exactly to EKF/IEKF/REKF/RIEKF, while SW-FGO shows measurable benefits in nonlinear, non-Gaussian regimes at a predictable compute cost. Finally, after clarifying the connection between them, we highlight the unique advantages of SW-FGO in practical phases, especially on numerical estimation and deep learning integration. The code and data used in this work is open sourced at https://github.com/Baoshan-Song/KFV-FGO-Comparison.","short_abstract":"Sliding window-factor graph optimization (SW-FGO) has gained more and more attention in navigation research due to its robust approximation to non-Gaussian noises and nonlinearity of measuring models. There are lots of works focusing on its application performance compared to extended Kalman filter (EKF) but there is s...","url_abs":"https://arxiv.org/abs/2511.00306","url_pdf":"https://arxiv.org/pdf/2511.00306v1","authors":"[\"Baoshan Song\",\"Ruijie Xu\",\"Li-Ta Hsu\"]","published":"2025-10-31T23:10:20Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false,"code_links":[{"ID":607560,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2847802,"paper_url":"https://arxiv.org/abs/2511.00306","paper_title":"FGO MythBusters: Explaining how Kalman Filter variants achieve the same performance as FGO in navigation applications","repo_url":"https://github.com/Baoshan-Song/KFV-FGO-Comparison","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}