Scholarlink

<p>We present a novel method that, given a grid map of a partially explored indoor environment, estimates the amount of the explored area in the map and whether it is worth continuing to explore the uncovered part of the environment. Our method is based on the idea that modern deep learning models can successfully solve this task by leveraging visual clues in the map. Thus, we train a deep convolutional neural network on images depicting grid maps from partially explored environments, with annotations derived from the knowledge of the entire map, which is not available when the network is used for inference. We show that our network can be used to define a stopping criterion to successfully terminate the exploration process when this is expected to no longer add relevant details about the environment to the map, saving more than 35% of the total exploration time compared to covering the whole environment area.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Estimating map completeness in robot exploration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月26日

查看全文
  • Matteo Luperto,
  • Marco Maria Ferrara,
  • Matteo Princisgh,
  • Giacomo Boracchi,
  • Francesco Amigoni

摘要

We present a novel method that, given a grid map of a partially explored indoor environment, estimates the amount of the explored area in the map and whether it is worth continuing to explore the uncovered part of the environment. Our method is based on the idea that modern deep learning models can successfully solve this task by leveraging visual clues in the map. Thus, we train a deep convolutional neural network on images depicting grid maps from partially explored environments, with annotations derived from the knowledge of the entire map, which is not available when the network is used for inference. We show that our network can be used to define a stopping criterion to successfully terminate the exploration process when this is expected to no longer add relevant details about the environment to the map, saving more than 35% of the total exploration time compared to covering the whole environment area.