A Heuristic Autonomous Exploration Method Based on Environmental Information Gain During Quadrotor Flight

TL;DR

This paper introduces a novel heuristic method for quadrotor autonomous exploration that significantly improves efficiency by faster frontier detection and an information gain-based viewpoint selection, validated through extensive tests.

Contribution

It presents a two-level viewpoint determination approach combining rapid frontier detection with an innovative information gain heuristic for better exploration efficiency.

Findings

Frontier search time reduced by 85%

Exploration time decreased by 20-30%

Exploration path optimized by 25-35%

Abstract

Autonomous exploration is a widely studied fundamental application in the field of quadrotors, which requires them to automatically explore unknown space to obtain complete information about the environment. The frontier-based method, which is one of the representative works on autonomous exploration, drives autonomous determination by the definition of frontier information, so that complete information about the environment is available to the quadrotor. However, existing frontier-based methods are able to accomplish the task but still suffer from inefficient exploration. How to improve the efficiency of autonomous exploration is the focus of current research. Typical problems include slow frontier generation, which affects real-time viewpoint determination, and insufficient determination methods that affect the quality of viewpoints. Therefore, to overcome these problems, this paper proposes a two-level viewpoint determination method for frontier-based autonomous exploration. Firstly, a sampling-based frontier detection method is presented for faster frontier generation, which improves the immediacy of environmental representation compared to traditional traversal-based methods. Secondly, we consider the access to environmental information during flight for the first time and design an innovative heuristic evaluation function to decide on a high-quality viewpoint as the next local navigation target in each exploration iteration. We conducted extensive benchmark and real-world tests to validate our method. The results confirm that our method optimizes the frontier search time by 85%, the exploration time by around 20-30%, and the exploration path by 25-35%.