AVIP: Acoustic-Visual-Inertial-Pressure Fusion-based Underwater Localization System with Multi-Centric Calibration

Abstract

Underwater localization is a crucial capability for ensuring robust and accurate vehicle navigation. Although various well-developed localization systems exist, their primary focus is on ground and aerial applications. The challenges posed by underwater environments, such as sparse textures and dynamic disturbances, enable the multi-modal fusion a promising solution for localization. This paper presents AVIP, a localization method that fuses Acoustic, Visual, Inertial, and Pressure modalities for underwater applications. To integrate the information from all modalities during initialization, visual and inertial modalities are alternately assigned as centric sensors to pairwise predict and update estimations of other modalities. The multi-centric calibration problem is addressed through factor graph optimization, which is fully integrated into the graph-based AVIP system as the calibration factor. To evaluate the performance and compare to state-of-the-art approaches, the proposed method is evaluated using semi-physical datasets recorded by a BlueROV2 robot and public real-world datasets. Extensive experiments demonstrate that AVIP achieves superior localization accuracy and exhibits adaptability across a range of sensor configurations.