用显著区域引导机器人探索，更快发现未知大区域。
Biasing Frontier-Based Exploration with Saliency Areas
- 基于神经网络生成显著图，识别高探索价值区域
- 在多个环境中验证，探索效率提升显著
- 适合需快速发现大未知区域的机器人任务
自主探索是机器人逐步构建未知环境地图的核心问题。机器人需在探索范围与速度间权衡。现有策略多以最大化探索面积为目标，但忽视环境不同区域的重要性差异——某些区域更可能通向大面积未知区域。本文提出通过神经网络生成的显著图，识别具有高探索价值的「显著区域」，并将其用于引导经典探索策略。实验表明，引入显著区域信息可显著改变机器人的探索行为，在多个场景下实现更高效的探索。该方法依赖于当前地图判断是否已完全探索的终止条件，从而动态评估环境重要性。
原文摘要 · Abstract (English)
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.
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