In this work, the authors propose a novel decentralised coordination scheme for autonomous ground vehicles to enable map building and path planning with a network of smart overhead cameras. Decentralised indoor smart camera mapping and hierarchical navigation supports the automatic generation of waypoint graphs for each camera in an environment and allows path planning through the environment across multiple camera fields of view, or subviews. The proposed solution utilises the growing neural gas algorithm to learn the topology of unoccupied working space in each subview for maintaining a dynamic waypoint graph on each camera. The authors’ pathing solution leverages a modified version of the A* algorithm to compute paths in a decentralised and hierarchical fashion. Waypoint generation was simulated and analysed on a generated environment to ensure it is both effective and efficient, while path planning was simulated on various randomised hierarchical graphs to effectively compare the proposed Decentralised-A* (D-A*) algorithm against standard greedy search. The proposed method efficiently handles the cases where other robot navigation methods are otherwise weak and ineffective, while still providing avenues for further optimisation of resource overhead for both the smart camera network as well as the robots themselves.

中文翻译：

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自主地面车辆的分散式室内智能摄像机映射和分层导航

在这项工作中，作者提出了一种用于自动地面车辆的新型分散式协调方案，以通过智能高架摄像机网络实现地图构建和路径规划。分散式室内智能摄像机映射和分层导航支持在环境中为每个摄像机自动生成航点图，并允许跨多个摄像机视场或子视图通过环境进行路径规划。所提出的解决方案利用增长的神经气体算法来学习每个子视图中未占用工作空间的拓扑，以便在每个摄像机上维护动态航路点图。作者的路径解决方案利用A *算法的修改版本以分散和分层的方式计算路径。在生成的环境中模拟并分析了航路点生成，以确保它既有效又高效，同时在各种随机层次图上模拟了路径规划，以有效地将拟议的Decentralized-A *（DA *）算法与标准贪婪搜索进行比较。所提出的方法有效地处理了其他机器人导航方法原本薄弱且无效的情况，同时仍为进一步优化智能相机网络以及机器人本身的资源开销提供了途径。