Efficient Autonomous Exploration With Incrementally Built Topological Map in 3-D Environments

Autonomous 3-D exploration with unmanned aerial vehicles (UAVs) is increasingly prevalent for environment monitoring without human intervention. In this article, we present a systematic solution toward efficient UAV exploration in 3-D environments. Innovatively, a road map is incrementally built and maintained along with the exploration process, which explicitly exhibits the topological structure of the 3-D environment. By simplifying the environment, the road map can efficiently provide the information gain and the cost-to-go for a candidate region to be explored, which are two quantities for next-best-view (NBV) evaluation, thus prompting the efficiency for NBV determination. In addition, with reference to the global plan queried on the road map, we propose a local planner based on the potential field method that drives the robot to the information-rich area during the navigation process, which further improves the exploration efficiency. The proposed framework and its composed modules are verified in various 3-D environments, which exhibit their distinctive features in NBV selection and better performance in improving the exploration efficiency than other methods.

I. Introduction

Recently, unmanned aerial vehicle (UAV) has been widely used in 3-D reconstruction, surveillance, and environment monitoring [1]–[3]. To persistently collect environment data, especially in cases where communication is infrequent or limited, it is essential that the UAV has the ability to explore the environment autonomously.