Robust Inertial-aided Underwater Localization based on Imaging Sonar Keyframes

TL;DR

This paper introduces a robust inertial-aided underwater localization method using imaging sonar keyframes, improving accuracy and robustness in feature-sparse environments for autonomous underwater vehicles.

Contribution

It proposes a novel inertial-aided sliding window optimization framework with sonar keyframes and a size-adjustable back-end, addressing under-constrained cases and landmark triangulation failures.

Findings

Outperforms existing methods in pose estimation accuracy

Enhances robustness against outliers and feature sparsity

Ensures real-time performance for online underwater navigation

Abstract

This article focuses on feature-based underwater localization and navigation for autonomous underwater vehicles (AUVs) using 2D imaging sonar measurements. The sparsity of underwater acoustic features and the loss of elevation angle in sonar images may introduce wrong feature matches or insufficient features for optimization-based underwater localization (i.e. under-constrained/degeneracy cases). This motivates us to propose a novel inertial-aided sliding window optimization framework to improve the estimation accuracy and the robustness to front-end outliers. Concretely, we first discriminate under-constrained/ well-constrained sonar frames and define sonar keyframes (SKFs) based on the Jacobian matrix derived from odometry and sonar measurements. To utilize the past well-constrained SKFs mostly, we design a size-adjustable windowed back-end optimization scheme based on singular values. We also prove that the landmark triangulation failure (navigation problem) caused by sonar motion can be solved in 2D scenes. Comparative simulation and evaluation on a public dataset show the proposed method outperforms the existing ones in pose estimation and robustness even without loop closure and also ensures the real-time performance for online applications.