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

arXiv · AI, language, vision and robotics · article · Sep 23, 2026 · UTC

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.

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