Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision

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.

Active SLAM for Autonomous Underwater Exploration

Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.

Guided Next Best View for 3D Reconstruction of Large Complex Structures

In this paper, a Next Best View (NBV) approach with a profiling stage and a novel utility function for 3D reconstruction using an Unmanned Aerial Vehicle (UAV) is proposed. The proposed approach performs an initial scan in order to build a rough model of the structure that is later used to improve coverage completeness and reduce flight time. Then, a more thorough NBV process is initiated, utilizing the rough model in order to create a dense 3D reconstruction of the structure of interest. The proposed approach exploits the reflectional symmetry feature if it exists in the initial scan of the structure. The proposed NBV approach is implemented with a novel utility function, which consists of four main components: information theory, model density, traveled distance, and predictive measures based on symmetries in the structure. This system outperforms classic information gain approaches with a higher density, entropy reduction and coverage completeness. Simulated and real experiments were conducted and the results show the effectiveness and applicability of the proposed approach.