ECHO: An Efficient Heuristic Viewpoint Determination Method on Frontier-Based Autonomous Exploration for Quadrotors

As a popular drone application, autonomous exploration suffers from low efficiency. To address the issue of repeated and unnecessary exploration, especially in a large-scale and cluttered environment, this letter proposes an efficient heuristic viewpoint determination method on frontier-based autonomous exploration, which includes viewpoint generation, evaluation, and refinement. A Gaussian sampler is employed to randomly generate higher-quality initial viewpoints; meanwhile, a fresh heuristic evaluation function is designed to select the next viewpoint; besides, a refinement strategy is presented to improve the viewpoint. Extensive simulations and real-world tests indicate that the proposed method outperforms the state-of-the-art frontier-based method by 15%-25% in almost all scenarios.

Automated summary

This letter proposes an efficient heuristic viewpoint determination method for autonomous exploration, which addresses the low efficiency issue. By randomly generating higher-quality initial viewpoints using a Gaussian sampler, selecting the next viewpoint with a fresh heuristic evaluation function, and refining the viewpoint, the proposed method outperforms the state-of-the-art frontier-based method by 15%-25% in almost all scenarios, as indicated by extensive simulations and real-world tests.