GeVI-SLAM: Gravity-Enhanced Stereo Visua Inertial SLAM for Underwater Robots

TL;DR

GeVI-SLAM introduces a gravity-enhanced stereo visual-inertial SLAM system for underwater robots that improves stability and accuracy by leveraging gravity information and a minimal 3-point solver, addressing visual degeneracy and low IMU excitation.

Contribution

The paper proposes a novel gravity-enhanced stereo VI SLAM system that decouples gravity from pose estimation, uses a minimal 3-point solver, and introduces a bias-eliminated 4-DOF PnP estimator for improved underwater robot localization.

Findings

Achieves higher accuracy than state-of-the-art methods.

Demonstrates stable operation under low acceleration dynamics.

Reduces computational time with a minimal 3-point solver.

Abstract

Accurate visual inertial simultaneous localization and mapping (VI SLAM) for underwater robots remains a significant challenge due to frequent visual degeneracy and insufficient inertial measurement unit (IMU) motion excitation. In this paper, we present GeVI-SLAM, a gravity-enhanced stereo VI SLAM system designed to address these issues. By leveraging the stereo camera's direct depth estimation ability, we eliminate the need to estimate scale during IMU initialization, enabling stable operation even under low acceleration dynamics. With precise gravity initialization, we decouple the pitch and roll from the pose estimation and solve a 4 degrees of freedom (DOF) Perspective-n-Point (PnP) problem for pose tracking. This allows the use of a minimal 3-point solver, which significantly reduces computational time to reject outliers within a Random Sample Consensus framework. We further propose a bias-eliminated 4-DOF PnP estimator with provable consistency, ensuring the relative pose converges to the true value as the feature number increases. To handle dynamic motion, we refine the full 6-DOF pose while jointly estimating the IMU covariance, enabling adaptive weighting of the gravity prior. Extensive experiments on simulated and real-world data demonstrate that GeVI-SLAM achieves higher accuracy and greater stability compared to state-of-the-art methods.