Biasing Frontier-Based Exploration with Saliency Areas

2025-08-14

AI Summary

To address the challenge of balancing comprehensiveness and efficiency in autonomous exploration, this paper proposes a saliency-aware frontier-based exploration method. The approach employs a lightweight neural network to generate an environment saliency map, highlighting regions with higher potential for unobserved space; this map is integrated into a classical frontier-point detection framework to dynamically weight candidate frontiers and—novelly—is leveraged for exploration termination decision-making. By synergizing deep learning–based attention mechanisms with traditional geometric mapping, the method operates without requiring semantic annotations. Extensive experiments across diverse simulated and real-world environments demonstrate that, compared to baseline approaches, the proposed method reduces exploration completion time by 23.7% on average and shortens traversal path length by 19.4%, significantly enhancing both exploration efficiency and robustness.

Technology Category

• Search and Optimization: Learning to Search
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Balancing exploration objectives in unknown environments
• Identifying high-interest saliency areas for efficient exploration
• Biasing exploration strategies using saliency maps for faster coverage

Innovation

• Uses saliency maps for exploration prioritization
• Biases exploration strategies with saliency areas
• Neural network estimates exploration termination