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

TL;DR

This paper introduces a scalable 3D exploration planner using frontier detection and clustering with Octree representations, optimized for large lidar point clouds, demonstrated through simulation and outdoor UAV tests.

Contribution

The novel planner improves scalability and efficiency by leveraging Octree-based clustering and mapping data instead of raw sensor data, enabling faster exploration.

Findings

More scalable than existing methods

Requires less processing time for large datasets

Effective in outdoor UAV exploration

Abstract

In this paper we propose a planner for 3D exploration that is suitable for applications using state-of-the-art 3D sensors such as lidars, which produce large point clouds with each scan. The planner is based on the detection of a frontier - a boundary between the explored and unknown part of the environment - and consists of the algorithm for detecting frontier points, followed by clustering of frontier points and selecting 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 data set size while ensuring similar exploration time. Performance is achieved by not relying on data obtained directly from the 3D sensor, but on data obtained by a mapping algorithm. In order to cluster the frontier points, we use 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.