一种基于采样的概率占用栅格层次信息论压缩方法
A Sample-Based Approach for Hierarchical Information-Theoretic Compression of Probabilistic Occupancy Grids
- University of Arizona（亚利桑那大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一种基于采样的层次信息论压缩框架，通过统计估计替代穷举递归，实现大规模概率占用栅格的快速信息驱动抽象，具有任意时间特性。
AI中文摘要：
我们开发了一种基于采样的框架，用于构建概率占用栅格的信息驱动层次多分辨率表示。近期方法通过基于动态规划的穷举递归计算信息最优抽象，这对于大规模栅格在计算上变得不可行，且不适合机器人应用。为解决这一局限，我们引入了一种受蒙特卡洛树搜索（MCTS）启发的基于采样的策略，通过统计估计而非穷举枚举来增量构建层次抽象。所提出的方法具有任意时间特性，允许在任何阶段终止计算以生成有效的压缩表示。我们将我们的方法与信息最优的Q树搜索算法进行比较，并展示了其在快速生成大规模真实世界概率占用栅格抽象方面的有效性。
英文摘要：
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.
