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

TL;DR

This paper introduces a multi-view landmark representation method for GNSS-Visual-Inertial Odometry that improves computational efficiency and accuracy by directly associating landmarks with multiple camera poses, validated through simulations and real-world tests.

Contribution

It proposes a novel pose-only measurement model that tightly couples landmarks with multiple camera poses, enhancing efficiency in sensor fusion applications.

Findings

Improved efficiency over traditional methods.

Enhanced accuracy in pose estimation.

Validated through simulations and real-world experiments.

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.