# Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
## 1 Introduction  
Unmanned aerial vehicles (UAV) are continuously increasing in value as tools and are useful aids for a multitude of human activities. These activities, scenarios, and concepts nowadays cover a wide range of contexts from the multi-vehicle oceanographic environment [1; 2; 3] to smart farming [4; 5], surveillance [6; 7; 8; 9], wildfire tracking [10; 11], or transportation [12; 13]. These systems have, through miniaturization and reduction in cost, become more attractive and viable especially for tasks which might pose a danger to humans or tasks which are, in their essence, logistical burdens to carry out continuously in a manual manner. The reality is that recent technological developments have opened the use of aerial robots to a broader public with UAVs now benefiting from off-the-shelve long-range wireless communications, high-resolution light sensors, on-board computation capacity, and power-efficient hardware. A prime example of these cumbersome and possibly dangerous tasks is that of mapping unexplored and unstructured areas. In this regard, the capabilities of UAVs are exceptional for scene reconstruction [14; 15; 16], environmental monitoring [10; 17; 18], industrial inspection [19; 20; 21; 22], bridge inspection [23; 24], and nuclear radiation detection [25] due to the increasing assortment of payloads that these vehicles can accommodate as well as the level of redundancy achievable by UAVs deployed in coordinated teams. In this work, our goals are two-fold: (1) Enabling the exploration of vast structures with no previous knowledge, and (2) using a low-cost, light laser. The simulated operational scenario chosen to experiment the exploration approach is the structural inspection in an industrial setting (see Figure 1). We showcase a system that combines well-known tools with a new sampling manoeuvre to use the potential range of a 2D laser sensor for 3D sampling. The system integrates a conservative path-planner with a layered exploration strategy that considers both the observation manoeuvre and a safety distance, thus leveraging both the operational objectives and the platform's safety. These distances and the overall size of the search space are addressed by clearly distinguishing between local space (within sensor range) and global space (the full search space). The sampling locations are discovered by searching for frontiers, voxels in the known free space that have unknown neighbours. The frontiers' algorithm is only the first step towards selecting a goal, with neighbouring unknown points targeted for inspection only when an inspection manoeuvre is possible and safe for the platform to perform. After the system identifies the locations with information gain, the sampling manoeuvre is adjusted based on the map configuration and the sensor range. The path planning time is reduced by adjusting the maximum search time to the scope of the exploration (local or global) and by only selecting goals that have a high likelihood of being reachable. The presented system is an extensible solution, applicable to other environments and operational scenarios beyond exploration. A significant takeaway of our work is the bridging of the gap between academic state-of-the-art equipment and tools currently available to industry. Considering that industrial structures can reach considerable dimension and magnitude, the ability to employ a systematic and autonomous solution which produces results within a reasonable time opens the door to lower operational costs. However, the generation of a flight plan that yields full coverage of a complex infrastructure remains challenging as sensor characteristics, UAV velocity, and error sources must be jointly taken into account to avoid multiple flights. UAV inspection as a service, has already achieved mainstream status and the value of incorporating our autonomous 3D inspections is clear, avoiding the escalation of current UAV service costs.