论文/arXiv 2609.27330·1 个月前

一种用于概率占据栅格分层信息论压缩的基于采样的方法

针对大规模概率占据栅格压缩计算开销高，提出受 MCTS 启发的采样式分层多分辨率表示，并在真实大规模栅格上验证。

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原文链接：A Sample-Based Approach for Hierarchical Information-Theoretic Compression of Probabilistic Occupancy Grids（arXiv 2609.27330 · 发布日期：2026-09-23）

A Sample-Based Approach for Hierarchical Information-Theoretic Compression of Probabilistic Occupancy Grids

针对大规模概率占据栅格压缩计算开销高，提出受 MCTS 启发的采样式分层多分辨率表示，并在真实大规模栅格上验证。

核心要点

• 现有动态规划穷举递归可得信息最优抽象，但大规模栅格计算代价过高。
• 方法借鉴 MCTS，以统计估计逐步构建分层抽象，避免穷举枚举。
• 该框架具备 anytime 特性，可在任意阶段停止并输出有效压缩表示。
• 实验与信息最优 Q-tree 搜索比较，能快速生成大型真实栅格抽象。

验证范围与局限：验证范围限于大型真实概率占据栅格的抽象生成对比实验。

英文摘要 (Abstract)

We develop a sample-based framework for constructing information-driven hierarchical multi-resolution representations of probabilistic occupancy grids. Recent methods compute information-optimal abstractions via dynamic-programming-based exhaustive recursions, which become computationally prohibitive for large-scale grids and are ill-suited to robotics applications. To address this limitation, we introduce a sample-based strategy inspired by Monte Carlo Tree Search (MCTS) that incrementally constructs hierarchical abstractions through statistical estimation rather than exhaustive enumeration. The proposed method is anytime in nature, allowing computation to be terminated at any stage to produce a valid compressed representation. We compare our approach with the information-optimal Q-tree search algorithm and demonstrate its effectiveness in rapidly generating abstractions of large real-world probabilistic occupancy grids.

作者：Zhenyu Jin, Daniel T. Larsson

来源

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1. arXiv 2609.27330：A Sample-Based Approach for Hierarchical Information-Theoretic Compression of Probabilistic Occupancy Grids访问 2026-10-21