Model Predictive Manhole Traversal with LiDAR-based View Synthesis

Autonomous inspection of maritime vessels using Unmanned Aerial Vehicles can prove difficult due to low light conditions, minimum space and battery limitations. This paper proposes a strategy for optimized manhole traversal in the scenario of maritime vessel ballast tank inspection. A dense point cloud, collected during the exploration of the ballast tank, is used to localize the manhole that the UAV has to traverse through. The prediction of the manhole is performed on synthesized 360° panoramic views of the point cloud, originating from positions inside the mapped area. This manhole prediction enables a Model Predictive Control framework to find the optimal path through the manhole, taking into account the capabilities of the UAV and the topological constraints of the confined space. The manhole localization method is able to predict the center of the manhole with an average success rate of $89.6 \%$, which can be raised to $94 \%$ after the ensembling of multiple predictions from different assumed camera positions. Simulated results show the robustness of the Model Predictive Control method in a wide array of different scenarios. We demonstrate the ability of the method to predict accurately the manhole and efficiently pass through it using a UAV, in a real-world experimental setup.