Learning Underwater Active Perception in Simulation
This work addresses the challenge of autonomous underwater inspection for robotics, though it is incremental as it builds on prior methods for adapting to water conditions.
The paper tackles the problem of underwater visual inspection by proposing an active perception framework that predicts image quality based on distance and light intensity, trained on a synthetic dataset of ten water types, and shows significant improvements in visual coverage and image quality compared to traditional methods.
When employing underwater vehicles for the autonomous inspection of assets, it is crucial to consider and assess the water conditions. Indeed, they have a significant impact on the visibility, which also affects robotic operations. Turbidity can jeopardise the whole mission as it may prevent correct visual documentation of the inspected structures. Previous works have introduced methods to adapt to turbidity and backscattering, however, they also include manoeuvring and setup constraints. We propose a simple yet efficient approach to enable high-quality image acquisition of assets in a broad range of water conditions. This active perception framework includes a multi-layer perceptron (MLP) trained to predict image quality given a distance to a target and artificial light intensity. We generated a large synthetic dataset including ten water types with different levels of turbidity and backscattering. For this, we modified the modelling software Blender to better account for the underwater light propagation properties. We validated the approach in simulation and showed significant improvements in visual coverage and quality of imagery compared to traditional approaches. The project code is available on our project page at https://roboticimaging.org/Projects/ActiveUW/.