# Communication-aware Hierarchical Map Compression of Time-Varying Environments for Mobile Robots

arXiv: https://arxiv.org/abs/2504.10751 · HTML: https://mlsanity.com/paper/2504.10751/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [Information-Theoretic Abstractions for Planning in Agents with Computational Constraints](https://mlsanity.com/paper/2005.09611/) (2020)
- [Reduced Complexity Multi-Scale Path-Planning on Probabilistic Maps](https://mlsanity.com/paper/1602.04800/) (2016)
- [Information-Theoretic Abstractions for Resource-Constrained Agents via Mixed-Integer Linear Programming](https://mlsanity.com/paper/2102.10015/) (2021)
- [Multi-agent Task-Driven Exploration via Intelligent Map Compression and Sharing](https://mlsanity.com/paper/2403.14780/) (2024)
- [Optimal Controller and Quantizer Selection for Partially Observable Linear-Quadratic-Gaussian Systems](https://mlsanity.com/paper/1909.13609/) (2019)
- [A Linear Programming Approach for Resource-Aware Information-Theoretic Tree Abstractions](https://mlsanity.com/paper/2208.04220/) (2022)
- [Communication-Aware Map Compression for Online Path-Planning](https://mlsanity.com/paper/2309.13451/) (2023)

## Abstract (arXiv)

In this paper, we develop a systematic framework for the time-sequential compression of dynamic probabilistic occupancy grids. Our approach leverages ideas from signal compression theory to formulate an optimization problem that searches for a multi-resolution hierarchical encoder that balances the quality of the compressed map (distortion) with its description size, the latter of which relates to the bandwidth required to reliably transmit the map to other agents or to store map estimates in on-board memory. The resulting optimization problem allows for multi-resolution map compressions to be obtained that satisfy available communication or memory resources, and does not require knowledge of the occupancy map dynamics. We develop an algorithm to solve our problem, and demonstrate the utility of the proposed framework in simulation on both static (i.e., non-time varying) and dynamic (time-varying) occupancy maps.