Iterative Smoothing and Outlier Detection for Underwater Navigation

TL;DR

This paper introduces an iterative smoothing and outlier detection method tailored for underwater visual-inertial navigation, effectively handling poor visibility and outliers to improve navigation accuracy in challenging underwater environments.

Contribution

The paper presents a novel iterative approach that does not rely on highly accurate inertial odometry, suitable for low-cost inertial systems in underwater navigation.

Findings

Successfully eliminates outliers in underwater navigation data

Enhances navigation accuracy in challenging underwater conditions

Validated with real underwater robot dataset

Abstract

Underwater visual-inertial navigation is challenging due to the poor visibility and presence of outliers in underwater environments. The navigation performance is closely related to outlier detection and elimination. Existing methods assume the inertial odometry is accurate enough for outlier detection, which is not valid for low-cost inertial applications. We propose a novel iterative smoothing and outlier detection method aiming for underwater navigation. Using the dataset collected from an underwater robot and fiducial markers, experimental results confirm that the method can successfully eliminate the outliers and enhance navigation accuracy.