AquaticVision: Benchmarking Visual SLAM in Underwater Environment with Events and Frames

TL;DR

AquaticVision introduces a comprehensive underwater visual SLAM dataset with ground truth trajectories and combined event and frame data, enabling more accurate benchmarking and development of robust underwater SLAM algorithms.

Contribution

It provides the first underwater dataset with ground truth and combined event-frame visual data for SLAM benchmarking, addressing previous data limitations.

Findings

First underwater dataset with ground truth trajectories.

Includes both event and frame visual data for SLAM benchmarking.

Facilitates development of robust underwater visual SLAM algorithms.

Abstract

Many underwater applications, such as offshore asset inspections, rely on visual inspection and detailed 3D reconstruction. Recent advancements in underwater visual SLAM systems for aquatic environments have garnered significant attention in marine robotics research. However, existing underwater visual SLAM datasets often lack groundtruth trajectory data, making it difficult to objectively compare the performance of different SLAM algorithms based solely on qualitative results or COLMAP reconstruction. In this paper, we present a novel underwater dataset that includes ground truth trajectory data obtained using a motion capture system. Additionally, for the first time, we release visual data that includes both events and frames for benchmarking underwater visual positioning. By providing event camera data, we aim to facilitate the development of more robust and advanced underwater visual SLAM algorithms. The use of event cameras can help mitigate challenges posed by extremely low light or hazy underwater conditions. The webpage of our dataset is https://sites.google.com/view/aquaticvision-lias.