A Multi-Resolution Frontier-Based Planner for Autonomous 3D Exploration

In this letter we propose a planner for 3D exploration that is suitable for applications using state-of-the-art 3D sensors, such as LiDARs, that produce large point clouds with each scan. The planner is based on the detection of a frontier - a boundary between the explored and the unknown part of the environment - and consists of the algorithm for detecting frontier points, followed by the clustering of frontier points and the selection of the best frontier point to be explored. Compared to existing frontier-based approaches, the planner is more scalable, i.e., it requires less time for the same environment size while ensuring similar exploration time. The performance is achieved by relying not on data obtained directly from the 3D sensor, but on data obtained by a mapping algorithm. In order to cluster the frontier points, we exploit the properties of the Octree environment representation, which allows easy analysis with different resolutions. The planner is tested in the simulation environment and in an outdoor test area with a UAV equipped with a LiDAR sensor. The results show the advantages of the approach compared to current state-of-the-art approaches.

Automated summary

The letter proposes a planner for 3D exploration using state-of-the-art sensors like LiDAR, based on frontier detection and clustering techniques to select the best points for exploration. The planner is more scalable than existing approaches, relying on mapping algorithm data and utilizing Octree environment representation for frontier point clustering. Tested in simulation and outdoor environments, the results demonstrate advantages over current approaches.