{"ID":2885228,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.05368","arxiv_id":"2508.05368","title":"A Multi-view Landmark Representation Approach with Application to GNSS-Visual-Inertial Odometry","abstract":"Invariant Extended Kalman Filter (IEKF) has been a significant technique in vision-aided sensor fusion. However, it usually suffers from high computational burden when jointly optimizing camera poses and the landmarks. To improve its efficiency and applicability for multi-sensor fusion, we present a multi-view pose-only estimation approach with its application to GNSS-Visual-Inertial Odometry (GVIO) in this paper. Our main contribution is deriving a visual measurement model which directly associates landmark representation with multiple camera poses and observations. Such a pose-only measurement is proven to be tightly-coupled between landmarks and poses, and maintain a perfect null space that is independent of estimated poses. Finally, we apply the proposed approach to a filter based GVIO with a novel feature management strategy. Both simulation tests and real-world experiments are conducted to demonstrate the superiority of the proposed method in terms of efficiency and accuracy.","short_abstract":"Invariant Extended Kalman Filter (IEKF) has been a significant technique in vision-aided sensor fusion. However, it usually suffers from high computational burden when jointly optimizing camera poses and the landmarks. To improve its efficiency and applicability for multi-sensor fusion, we present a multi-view pose-onl...","url_abs":"https://arxiv.org/abs/2508.05368","url_pdf":"https://arxiv.org/pdf/2508.05368v1","authors":"[\"Tong Hua\",\"Jiale Han\",\"Wei Ouyang\"]","published":"2025-08-07T13:14:35Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"eess.SY\"]","methods":"[]","has_code":false}