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Conference Papers Year : 2020

Next-Best-View planning for surface reconstruction of large-scale 3D environments with multiple UAVs

Abstract

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.
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Dates and versions

hal-03086499 , version 1 (22-12-2020)

Identifiers

  • HAL Id : hal-03086499 , version 1

Cite

Guillaume Hardouin, Julien Moras, Fabio Morbidi, Julien Marzat, El Mustapha Mouaddib. Next-Best-View planning for surface reconstruction of large-scale 3D environments with multiple UAVs. IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2020, Las Vegas, United States. pp.1567-1574. ⟨hal-03086499⟩
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