用路径信息增益与地图预测提升室内机器人探索效率
PIPE Planner: Pathwise Information Gain with Map Predictions for Indoor Robot Exploration
- 沿路径积分信息增益，结合地图预测减少过估计
- 计算路径上预期观测掩码，降低计算开销
- 在真实平面图数据集上优于现有方法
自主探索未知环境需要评估动作的信息增益以指导规划。以往方法通常在离散路点计算信息增益，而路径积分能更全面估计，但常因计算复杂或不可行且易过估计。本文提出面向探索的路径信息增益与地图预测（PIPE）规划器，结合路径累积传感器覆盖与地图预测，缓解过估计问题。为实现高效路径覆盖计算，提出一种高效计算计划路径上期望观测掩码的方法，显著降低计算开销。在真实楼层平面图数据集上验证，结果表明其性能优于当前最优基线。研究揭示了将预测映射与路径信息增益融合在高效、智能探索中的优势。
原文摘要 · Abstract (English)
Autonomous exploration in unknown environments requires estimating the information gain of an action to guide planning decisions. While prior approaches often compute information gain at discrete waypoints, pathwise integration offers a more comprehensive estimation but is often computationally challenging or infeasible and prone to overestimation. In this work, we propose the Pathwise Information Gain with Map Prediction for Exploration (PIPE) planner, which integrates cumulative sensor coverage along planned trajectories while leveraging map prediction to mitigate overestimation. To enable efficient pathwise coverage computation, we introduce a method to efficiently calculate the expected observation mask along the planned path, significantly reducing computational overhead. We validate PIPE on real-world floorplan datasets, demonstrating its superior performance over state-of-the-art baselines. Our results highlight the benefits of integrating predictive mapping with pathwise information gain for efficient and informed exploration. Website: https://pipe-planner.github.io
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