Overhead Image Factors for Underwater Sonar-based SLAM

TL;DR

This paper introduces a novel method that leverages overhead imagery and CNNs to improve underwater SLAM accuracy by registering sonar images with global overhead maps, validated through simulations and real deployments.

Contribution

It proposes a new approach combining CNN-based synthetic overhead images with pose graph SLAM to enhance underwater vehicle localization accuracy.

Findings

Improved accuracy in underwater SLAM using overhead imagery.

Validated approach through simulations and real-world deployment.

Quantitative results show significant localization improvements.

Abstract

Simultaneous localization and mapping (SLAM) is a critical capability for any autonomous underwater vehicle (AUV). However, robust, accurate state estimation is still a work in progress when using low-cost sensors. We propose enhancing a typical low-cost sensor package using widely available and often free prior information; overhead imagery. Given an AUV's sonar image and a partially overlapping, globally-referenced overhead image, we propose using a convolutional neural network (CNN) to generate a synthetic overhead image predicting the above-surface appearance of the sonar image contents. We then use this synthetic overhead image to register our observations to the provided global overhead image. Once registered, the transformation is introduced as a factor into a pose SLAM factor graph. We use a state-of-the-art simulation environment to perform validation over a series of benchmark trajectories and quantitatively show the improved accuracy of robot state estimation using the proposed approach. We also show qualitative outcomes from a real AUV field deployment. Video attachment: https://youtu.be/_uWljtp58ks