In this paper, a novel approach is introduced which utilizes a Rapidly-exploring Random Graph to improve sampling-based autonomous exploration of unknown environments with unmanned ground vehicles compared to the current state of the art. Its intended usage is in rescue scenarios in large indoor and underground environments with limited teleoperation ability. Local and global sampling are used to improve the exploration efficiency for large environments. Nodes are selected as the next exploration goal based on a gain-cost ratio derived from the assumed 3D map coverage at the particular node and the distance to it. The proposed approach features a continuously-built graph with a decoupled calculation of node gains using a computationally efficient ray tracing method. The Next-Best View is evaluated while the robot is pursuing a goal, which eliminates the need to wait for gain calculation after reaching the previous goal and significantly speeds up the exploration. Furthermore, a grid map is used to determine the traversability between the nodes in the graph while also providing a global plan for navigating towards selected goals. Simulations compare the proposed approach to state-of-the-art exploration algorithms and demonstrate its superior performance.

中文翻译：

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地面车辆的快速探索随机图次佳视图探索

在本文中，介绍了一种新方法，该方法利用快速探索随机图来改进基于采样的无人驾驶地面车辆未知环境的自主探索，与当前最先进的技术相比。其预期用途是用于远程操作能力有限的大型室内和地下环境的救援场景。局部和全局采样用于提高大型环境的探索效率。根据从特定节点处假定的 3D 地图覆盖范围及其到它的距离得出的增益成本比，选择节点作为下一个探索目标。所提出的方法具有使用计算效率高的光线跟踪方法对节点增益进行解耦计算的连续构建图。在机器人追求目标的同时评估 Next-Best View，这消除了在达到之前的目标后等待增益计算的需要，并显着加快了探索速度。此外，网格图用于确定图中节点之间的可遍历性，同时还提供导航到选定目标的全局计划。模拟将所提出的方法与最先进的探索算法进行了比较，并展示了其卓越的性能。