用概率信息增益提升室内探索效率，兼顾预测与感知不确定性。
MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions
- 基于预测地图构建概率传感器模型，融合不确定性和可视区域
- 在KTH数据集上比现有方法平均提升12.4%，较最近邻前缘法提升25.4%
- 适合需要高效结构化环境探索的机器人系统开发者
探索是机器人理解未知环境的核心挑战。本文聚焦于具有可预测结构的室内场景，这类环境常包含重复模式。传统方法如前沿搜索依赖简单启发式策略（如最近优先），难以利用环境规律性。近期工作虽引入深度学习预测未知地图以计算信息增益，但对预测质量敏感且忽略传感器覆盖范围。为此，我们提出联合推理机器人可观测性与不确定性，计算概率信息增益。MapEx通过多张预测地图，结合预测方差与估计可视面积，评估视角的信息增益。在真实世界KTH数据集上的实验表明，该方法相比代表性地图预测探索平均提升12.4%，较最近邻前缘方法提升25.4%。
原文摘要 · Abstract (English)
Exploration is a critical challenge in robotics, centered on understanding unknown environments. In this work, we focus on robots exploring structured indoor environments which are often predictable and composed of repeating patterns. Most existing approaches, such as conventional frontier approaches, have difficulty leveraging the predictability and explore with simple heuristics such as `closest first'. Recent works use deep learning techniques to predict unknown regions of the map, using these predictions for information gain calculation. However, these approaches are often sensitive to the predicted map quality or do not reason over sensor coverage. To overcome these issues, our key insight is to jointly reason over what the robot can observe and its uncertainty to calculate probabilistic information gain. We introduce MapEx, a new exploration framework that uses predicted maps to form probabilistic sensor model for information gain estimation. MapEx generates multiple predicted maps based on observed information, and takes into consideration both the computed variances of predicted maps and estimated visible area to estimate the information gain of a given viewpoint. Experiments on the real-world KTH dataset showed on average 12.4% improvement than representative map-prediction based exploration and 25.4% improvement than nearest frontier approach. Website: mapex-explorer.github.io
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