Biasing Frontier-Based Exploration with Saliency Areas

TL;DR

This paper introduces a novel exploration strategy that uses saliency maps from neural networks to identify high-interest areas, guiding robots to explore more effectively by prioritizing important regions.

Contribution

It proposes a method to incorporate saliency areas into exploration strategies, improving the efficiency of autonomous exploration by focusing on more informative regions.

Findings

Saliency-based biasing improves exploration speed.

Neural network-derived saliency maps effectively identify key exploration areas.

Enhanced exploration behavior demonstrated through extensive experiments.

Abstract

Autonomous exploration is a widely studied problem where a robot incrementally builds a map of a previously unknown environment. The robot selects the next locations to reach using an exploration strategy. To do so, the robot has to balance between competing objectives, like exploring the entirety of the environment, while being as fast as possible. Most exploration strategies try to maximise the explored area to speed up exploration; however, they do not consider that parts of the environment are more important than others, as they lead to the discovery of large unknown areas. We propose a method that identifies \emph{saliency areas} as those areas that are of high interest for exploration, by using saliency maps obtained from a neural network that, given the current map, implements a termination criterion to estimate whether the environment can be considered fully-explored or not. We use saliency areas to bias some widely used exploration strategies, showing, with an extensive experimental campaign, that this knowledge can significantly influence the behavior of the robot during exploration.