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

Abstract

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.