robotic inspection teams

Abstract: We present a complete, GPU-resident pipeline for multi-robot 3D inspection of underwater structures. Given a triangle mesh and a fleet of $K$ robots, each carrying a short-range, limited field-of-view camera, the pipeline computes 6-DOF trajectories that jointly cover a target fraction of the surface while minimizing a linearly interpolated makespan/total-cost objective. The joint problem is NP-hard, so we decompose it into five core stages - candidate viewpoint generation, visibility computation, set cover, min-max vehicle routing solved as a mixed integer program (MIP) with lifted Miller-Tucker-Zemlin subtour elimination, and priority-based multi-agent path finding with continuous-time inter-robot conflict checking - flanked by mesh loading and surface sampling as preprocessing and by trajectory smoothing as postprocessing. Our contributions are: an end-to-end GPU-accelerated integration of the five stages, with its kernel design documented; a GPU batch adaptation of Lien's $\varepsilon$-visibility with four custom CUDA kernels; a $β$-aware per-vehicle tour upper bound derived from the interpolated objective, supplementing the Desrochers-Laporte coefficient lift with forbidden-pair cuts; a 4D space-time extension of Zhou and Zeng's GPU parallel-frontier A*; and an experimental evaluation on 14 meshes (up to $7.2$M faces) that ablates the routing cuts and the parallel-frontier search, audits collision freedom and waypoint attainment \emph{after} trajectory smoothing, and measures the end-to-end gap against analytical and LP-relaxation lower bounds. Code and raw results are publicly available.

Abstract: Partially transmissive screens and protective covers are common in robotic inspection, but they create mixed LiDAR returns from both the foreground material and the scene behind it. Conventional peak-based LiDAR usually discards weak hidden returns, while single-photon LiDAR records time-resolved histograms that preserve attenuated and overlapping echoes. However, existing transient reconstruction methods typically fit a single scene representation to the measured waveform. Under occlusion, weak or nearby foreground--hidden echoes can form a broad peak or subtle shoulder. Because such waveforms can also be explained by a displaced single surface or a thick density distribution, accurate transient fitting does not necessarily imply correct geometry. We propose a state-aware framework for foreground-view and hidden scene reconstruction from occluded single-photon histograms. For each ray, we estimate local echo evidence, identifying no reliable surface evidence, single-return evidence, or two returns. The inferred echo state routes supervision for a two-head neural field: all rays constrain waveform reconstruction, while reliable anchors provide geometry localization. We also introduce a real paired single-photon LiDAR occlusion dataset with occluded and clean captures at fixed poses. Experiments on a real dataset show improved hidden scene depth and point-cloud accuracy over baselines. Our results demonstrate single-photon layered reconstruction as a practical route for 3D perception through partially transmissive occluders.