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.

本研究解决了动态概率占用网格的时间序列压缩问题，填补了现有方法在地图压缩质量与描述大小平衡方面的空白。通过信号压缩理论，提出了一种优化问题来实现多分辨率层次化地图编码，显著提升了地图在无线传输和存储过程中的效率。这一框架的有效性在静态与动态占用地图的仿真中得到了验证，表明其在实际应用中的潜在影响。

面向通信的移动机器人动态环境层次化地图压缩