VIMS: A Visual-Inertial-Magnetic-Sonar SLAM System in Underwater Environments

TL;DR

VIMS is a novel underwater SLAM system that combines visual, inertial, magnetic, and sonar data to improve localization and mapping accuracy in challenging underwater conditions.

Contribution

It introduces a low-cost sonar for scale estimation and a hierarchical magnetic place recognition scheme, enhancing robustness and efficiency over existing methods.

Findings

Significant improvement in localization accuracy

Enhanced robustness in perceptually degraded environments

Effective loop closure with hierarchical magnetic recognition

Abstract

In this study, we present a novel simultaneous localization and mapping (SLAM) system, VIMS, designed for underwater navigation. Conventional visual-inertial state estimators encounter significant practical challenges in perceptually degraded underwater environments, particularly in scale estimation and loop closing. To address these issues, we first propose leveraging a low-cost single-beam sonar to improve scale estimation. Then, VIMS integrates a high-sampling-rate magnetometer for place recognition by utilizing magnetic signatures generated by an economical magnetic field coil. Building on this, a hierarchical scheme is developed for visual-magnetic place recognition, enabling robust loop closure. Furthermore, VIMS achieves a balance between local feature tracking and descriptor-based loop closing, avoiding additional computational burden on the front end. Experimental results highlight the efficacy of the proposed VIMS, demonstrating significant improvements in both the robustness and accuracy of state estimation within underwater environments.