为移动机器人设计动态地图压缩框架，兼顾传输效率与地图质量。
Communication-aware Hierarchical Map Compression of Time-Varying Environments for Mobile Robots
- 基于信号压缩理论构建分层编码优化模型。
- 在不依赖地图动态先验下实现多分辨率压缩。
- 适合资源受限的机器人通信与存储场景。
本文提出一种针对时序动态概率占据栅格的系统性压缩框架。方法借鉴信号压缩理论，建立优化问题，寻找多分辨率分层编码器，在压缩地图保真度（失真）与描述长度（影响传输带宽或本地存储开销）之间取得平衡。该优化框架可生成满足通信或存储资源约束的地图压缩结果，且无需预先了解占据图的动态特性。我们设计了求解算法，并在静态与动态占据图的仿真中验证了该框架的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。