Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision

Abstract

Exploration of marine habitats is one of the key pillars of underwater science, which often involves collecting images at close range. As acquiring imagery close to the seabed involves multiple hazards, the safety of underwater vehicles, such as remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs), is often compromised. Common applications for obstacle avoidance in underwater environments are often conducted with acoustic sensors, which cannot be used reliably at very short distances, thus requiring a high level of attention from the operator to avoid damaging the robot. Therefore, developing capabilities such as advanced assisted mapping, spatial awareness and safety, and user immersion in confined environments is an important research area for human-operated underwater robotics. In this paper, we present a novel approach that provides an ROV with capabilities for navigation in complex environments. By leveraging the ability of omnidirectional multi-camera systems to provide a comprehensive view of the environment, we create a 360° real-time point cloud of nearby objects or structures within a visual SLAM framework. We also develop a strategy to assess the risk of obstacles in the vicinity. We show that the system can use the risk information to generate warnings that the robot can use to perform evasive maneuvers when approaching dangerous obstacles in real-world scenarios. This system is a first step towards a comprehensive pilot assistance system that will enable inexperienced pilots to operate vehicles in complex and cluttered environments.

A Review of Underwater Robot Localization in Confined Spaces

“…They tested visual SLAM localization results using a front-facing camera in underwater caves. E. Ochoa [96] and colleagues used four cameras installed in various directions on an underwater robot to collect environmental information. They employed SLAM to establish a 3D environmental model and achieve self-localization.…”

Advancing Data Quality of Marine Archaeological Documentation Using Underwater Robotics: From Simulation Environments to Real-World Scenarios

“…AUVs normally use a combination of acoustic positioning and dead reckoning, using Doppler Velocity Logs (DVL) and Inertial Measurement Units (IMU) for estimating speed and orientation respectively, while in some cases Simultaneous Localization and Mapping (SLAM) techniques are applied (Leonardi et al 2023). Research in the field of autonomous operations is focused on optimizing visual-based navigation through Visual SLAM (VSLAM) and Visual Odometry (VO) techniques (Williams et al 2016) and intelligent path planning methods that ensure full coverage of the surveying area (Karapetyan et al 2021) and collision-free trajectories (Ochoa et al 2022), even for scenarios of sites without any a priori knowledge of their environment. For shallow waters or coastal UCH sites, ASVs are demonstrated as suitable platforms for documentation and mapping purposes (Vasilijevic et al 2015).…”

3D reconstruction of spherical images: A review of techniques, applications, and prospects

“…However, they cannot work well at a close range. In the work of [154], an obstacle detection instrument has been designed by using a spherical camera, which can generate real-time point clouds from a visual SLAM system and assist the autonomous obstacle detection, as presented in Fig. 23(b).…”