Localization, Mapping and SLAM in Marine and Underwater Environments

The use of robots in marine and underwater applications is growing rapidly. These applications share the common requirement of modeling the environment and estimating the robots’ pose. Although there are several mapping, SLAM, target detection and localization methods, marine and underwater environments have several challenging characteristics, such as poor visibility, water currents, communication issues, sonar inaccuracies or unstructured environments, that have to be considered. The purpose of this Special Issue is to present the current research trends in the topics of underwater localization, mapping, SLAM, and target detection and localization. To this end, we have collected seven articles from leading researchers in the field, and present the different approaches and methods currently being investigated to improve the performance of underwater robots

DEEP LEARNING TO SUPPORT 3D MAPPING CAPABILITIES OF A PORTABLE VSLAM-BASED SYSTEM

The use of vision-based localization and mapping techniques, such as visual odometry and SLAM, has become increasingly prevalent in the field of Geomatics, particularly in mobile mapping systems. These methods provide real-time estimation of the 3D scene as well as sensor's position and orientation using images or LiDAR sensors mounted on a moving platform. While visual odometry primarily focuses on the camera's position, SLAM also creates a 3D reconstruction of the environment. Conventional (geometric) and learning-based approaches are used in visual SLAM, with deep learning networks being integrated to perform semantic segmentation, object detection and depth prediction. The goal of this work is to report ongoing developments to extend the GuPho stereo-vision SLAM-based system with deep learning networks for tasks such as crack detection, obstacle detection and depth estimation. Our findings show how a neural network can be coupled to SLAM sequences in order to support 3D mapping application with semantic information

3D reconstruction in underwater environment using CAD model alignment with images

Subsea assets need to be regularly inspected, maintained and repaired. These operations are typically performed using a Remotely Operated Vehicle (ROV) controlled by a pilot that sits in a ship. In order to make operations safer and cheaper, it would be interesting to control the ROVs from land, avoiding the need to hire a ship and crew. As part of these operations, ROVs need to perform high precision actions such as turning valves, which may be hard to perform in this remote setting due to latency. A semi-autonomous vehicle capable of performing high precision tasks could potentiate the transition to fully remote operations, where people stay on land. In order to develop such a system, we need a robust perception model capable of segmenting the assets of interest. Additionally, it is important to fuse that information with 3D models of those same assets in order to have a spatial perception of the environment. This fusion may be useful to, in the future, plan the necessary actions to interact with the given asset. The main goal of this work is to implement a model that: 1) segments different subsea assets of interest, such as valves; and 2) fuse the segmentation information with 3D models of those same assets