In this article, we propose an original solution to the problem of surface reconstruction of large-scale unknown environments, with multiple cooperative robots. As they progress through the 3-D environment, the robots rely on volumetric maps obtained via a TSDF representation to extract discrete incomplete surface elements (ISEs), and a list of candidate viewpoints is generated to cover them. A next-best-view planning approach, which approximately solves a traveling salesman problem (TSP) via greedy allocation, is then used to iteratively assign these viewpoints to the robots. Two multiagent architectures, a centralized one (TSP-Greedy Allocation or TSGA) and a distributed one (dist-TSGA), in which the robots locally compute their maps and share them, are developed and compared. Extensive numerical and real-world experiments with multiple aerial and ground robots in challenging 3-D environments show the flexibility and effectiveness of our surface representation of a volumetric map. The experiments also shed light on the nexus between reconstruction accuracy and surface completeness, and between total distance traveled and execution time.

In this paper, we propose a novel cluster-based Next-Best-View path planning algorithm to simultaneously explore and inspect large-scale unknown environments with multiple Unmanned Aerial Vehicles (UAVs). In the majority of existing informative path-planning methods, a volumetric criterion is used for the exploration of unknown areas, and the presence of surfaces is only taken into account indirectly. Unfortunately, this approach may lead to inaccurate 3D models, with no guarantee of global surface coverage. To perform accurate 3D reconstructions and minimize runtime, we extend our previous online planner based on TSDF (Truncated Signed Distance Function) mapping, to a fleet of UAVs. Sensor configurations to be visited are directly extracted from the map and assigned greedily to the aerial vehicles, in order to maximize the global utility at the fleet level. The performances of the proposed TSGA (TSP-Greedy Allocation) planner and of a nearest neighbor planner have been compared via realistic numerical experiments in two challenging environments (a power plant and the Statue of Liberty) with up to five quadrotor UAVs equipped with stereo cameras.