# Communication-Aware Map Compression for Online Path-Planning

arXiv: https://arxiv.org/abs/2309.13451 · HTML: https://mlsanity.com/paper/2309.13451/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2023.

## Built on by (most cited first, 1 of 1)

- [Communication-aware Hierarchical Map Compression of Time-Varying Environments for Mobile Robots](https://mlsanity.com/paper/2504.10751/) (2025)

## Abstract (arXiv)

This paper addresses the problem of the communication of optimally compressed information for mobile robot path-planning. In this context, mobile robots compress their current local maps to assist another robot in reaching a target in an unknown environment. We propose a framework that sequentially selects the optimal compression, guided by the robot's path, by balancing the map resolution and communication cost. Our approach is tractable in close-to-real scenarios and does not necessitate prior environment knowledge. We design a novel decoder that leverages compressed information to estimate the unknown environment via convex optimization with linear constraints and an encoder that utilizes the decoder to select the optimal compression. Numerical simulations are conducted in a large close-to-real map and a maze map and compared with two alternative approaches. The results confirm the effectiveness of our framework in assisting the robot reach its target by reducing transmitted information, on average, by approximately 50% while maintaining satisfactory performance.