# Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
## 7 Conclusions and Future Work  
This paper presented a deterministic system capable of autonomously inspecting large structures in unknown and heterogeneous scenarios. The resulting system combined well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure a 3D structure. This combination allowed the use of the far-reaching laser sensors instead of the more common depth camera. The sampling manoeuvre and the flyby extended the 2D range into 3D. The resulting flight plan explored 93% of the volume defined by the operator and covered most of the reachable occupied surface in the first 30 min. The effectiveness of conducting the reconnaissance of the scenario suggests having a two-step approach. The first step would be reconnaissance with the proposed approach and a second step to consolidate the measurements. In the second step, each vertex becomes the node of a graph of sampling locations, a Travelling Salesman Problem. The path of the UAV was continuous and without repetition, adapting in three dimensions to the free known space enabling the UAV to cover indoor locations, irregular structures, and suspended obstacles. Without prior knowledge of the world, the system sampled a scenario in a shorter period than the time required for a human operator to plan the path and fly the UAV while avoiding gaps in the coverage. The tests were conducted using a HitL environment identical to the environment used to validate Lazy Theta* in outdoors experimental campaigns in [44]. The exploration strategy successfully identified the locations of information gain with the frontier algorithm, taking advantage of the spatial organisation embedded into the octree to order the frontiers. First, the search for frontiers was done locally, to minimise the length of the flight path, then, if no frontier was available, the global exploration ensured a full coverage of the map. For each frontier, the exploration strategy searched for a flyby sampling manoeuvre that fit into the available free space. Maintaining a forward-facing sensor during the flight led to a maximisation of the information gained. Using the spatial organisation of the octree, the system was able to explore more than with any of the other heuristics considered in the paper. The heuristic employed to select the frontier is compared with other two approaches from the literature: The classic nearest neighbour approach and an occupied space centric heuristic. The median of the execution time of the three heuristics is similar but the surface frontier has a significantly smaller variability. However, at the end of the exploration, the entropy was markedly higher when using the surface frontier heuristic. The total volume explored reflected the entropy results and the surface frontiers heuristic achieved less known space. The deterministic property was relevant for industrial tools that require certification to the highest level. The UAV could function as a stand-alone tool since all the calculations were performed online and on-board. Although the use case in this paper considered a rotary-wing UAV industrial inspection, the system could be applied to other platforms such as AUVs and other applications such as humanitarian relief. Several avenues of future research are open. A more dynamic approach could adjust the number of flyby orientations according to the obstacles density for applications that need to minimisethe unreachable volume. Additionally, generating the flyby by clustering frontiers and finding an orthogonal vector removes the requirement of line-of-sight checks to assess each flyby. It would be interesting to explore the impact of more significant differentiation between the local and the global exploration strategy through the frontier heuristic, including the occupied neighbours and the nearest distance. Future works continue towards finding alternative means of maintaining the voxel variability while reducing the exploration execution time. Investigation, M.F. ; Resources, A.V. ; Software, H.P.-L.; Supervision, I.M.