A Sample-Based Approach for Hierarchical Information-Theoretic Compression of Probabilistic Occupancy Grids
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.
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BibTeX
@misc{jin2026samplebased,
title = {{A Sample-Based Approach for Hierarchical Information-Theoretic Compression of Probabilistic Occupancy Grids}},
author = {Zhenyu Jin and Daniel T. Larsson},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.27330},
eprint = {2609.27330},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.27330},
}
RIS
TY - UNPB
TI - A Sample-Based Approach for Hierarchical Information-Theoretic Compression of Probabilistic Occupancy Grids
AU - Zhenyu Jin
AU - Daniel T. Larsson
PY - 2026
DA - 2026/09/23
PB - arXiv
DO - 10.48550/arxiv.2609.27330
UR - https://arxiv.org/abs/2609.27330
AB - 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.
ER -