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arXiv · 2508.05368

A Multi-view Landmark Representation Approach with Application to GNSS-Visual-Inertial Odometry

Tong Hua·Jiale Han·Wei Ouyang
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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.

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Tong Hua, Jiale Han, Wei Ouyang. 2025-08-07. A Multi-view Landmark Representation Approach with Application to GNSS-Visual-Inertial Odometry. https://arxiv.org/abs/2508.05368

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