In this article, we propose a novel navigation framework that leverages a two layered graph representation of the environment for efficient large-scale exploration, while it integrates a novel uncertainty awareness scheme to handle dynamic scene changes in previously explored areas. The framework is structured around a novel goal oriented graph representation, that consists of, i) the local sub-graph and ii) the global graph layer respectively. The local sub-graphs encode local volumetric gain locations as frontiers, based on the direct pointcloud visibility, allowing fast graph building and path planning. Additionally, the global graph is build in an efficient way, using node-edge information exchange only on overlapping regions of sequential sub-graphs. Different from the state-of-the-art graph based exploration methods, the proposed approach efficiently re-uses sub-graphs built in previous iterations to construct the global navigation layer. Another merit of the proposed scheme is the ability to handle scene changes (e.g. blocked pathways), adaptively updating the obstructed part of the global graph from traversable to not-traversable. This operation involved oriented sample space of a path segment in the global graph layer, while removing the respective edges from connected nodes of the global graph in cases of obstructions. As such, the exploration behavior is directing the robot to follow another route in the global re-positioning phase through path-way updates in the global graph. Finally, we showcase the performance of the method both in simulation runs as well as deployed in real-world scene involving a legged robot carrying camera and lidar sensor.

翻译：本文提出一种新颖的导航框架，通过构建环境的两层图表示实现高效大规模探索，同时集成了一种新颖的不确定性感知机制以处理已探索区域中的动态场景变化。该框架围绕一种新颖的目标导向图表示构建，具体包括：i) 局部子图层和ii) 全局图层。局部子图基于直接点云可见性将局部体积增益位置编码为前沿点，支持快速图构建与路径规划。全局图则采用高效方式构建，仅通过连续子图重叠区域的节点-边信息交换实现。与现有基于图的探索方法不同，本方法高效复用先前迭代构建的子图来生成全局导航层。该方案的另一个优势在于能够处理场景变化（如路径阻塞），通过自适应更新全局图中受阻部分（从可通行状态转为不可通行状态）。此操作涉及全局图层中路径段的定向采样空间，并在发生阻塞时移除全局图连通节点间的对应边。由此，探索行为通过全局图中的路径更新，引导机器人在全局重定位阶段转向其他路线。最后，我们通过仿真实验以及搭载相机与激光雷达传感器的足式机器人在真实场景中的部署，验证了该方法的性能。