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Information based indoor environment robotic exploration and modeling using 2-D images and graphs

机译：使用二维图像和图形的基于信息的室内环境机器人探索和建模

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As the autonomy of personal service robotic systems increases so has their need to interact with their environment. The most basic interaction a robotic agent may have with its environment is to sense and navigate through it. For many applications it is not usually practical to provide robots in advance with valid geometric models of their environment. The robot will need to create these models by moving around and sensing the environment, while minimizing the complexity of the required sensing hardware. Here, an information-based iterative algorithm is proposed to plan the robot's visual exploration strategy, enabling it to most efficiently build a graph model of its environment. The algorithm is based on determining the information present in sub-regions of a 2-D panoramic image of the environment from the robot's current location using a single camera fixed on the mobile robot. Using a metric based on Shannon's information theory, the algorithm determines potential locations of nodes from which to further image the environment. Using a feature tracking process, the algorithm helps navigate the robot to each new node, where the imaging process is repeated. A Mellin transform and tracking process is used to guide the robot back to a previous node. This imaging, evaluation, branching and retracing its steps continues until the robot has mapped the environment to a pre-specified level of detail. The set of nodes and the images taken at each node are combined into a graph to model the environment. By tracing its path from node to node, a service robot can navigate around its environment. This method is particularly well suited for flat-floored environments. Experimental results show the effectiveness of this algorithm.
机译：随着个人服务机器人系统自主性的提高，它们与环境交互的需求也越来越大。机器人代理与其环境之间最基本的交互是感知和导航。对于许多应用程序来说，预先为机器人提供周围环境的有效几何模型通常是不实际的。机器人将需要通过移动和感应环境来创建这些模型，同时将所需感应硬件的复杂性降至最低。在此，提出了一种基于信息的迭代算法来规划机器人的视觉探索策略，从而使其能够最有效地构建其环境的图形模型。该算法基于使用固定在移动机器人上的单个摄像头从机器人的当前位置确定环境的二维全景图像的子区域中存在的信息。使用基于Shannon信息理论的度量，该算法确定了节点的潜在位置，可以从这些位置进一步对环境成像。通过使用特征跟踪过程，该算法有助于将机器人导航到每个新节点，并在其中重复成像过程。 Mellin变换和跟踪过程用于将机器人引导回上一个节点。这种成像，评估，分支和追溯其步骤一直持续到机器人将环境映射到预先指定的详细程度为止。节点集和在每个节点处拍摄的图像被组合到一个图形中以对环境建模。通过跟踪节点之间的路径，服务机器人可以在其环境中导航。此方法特别适合于地板平整的环境。实验结果证明了该算法的有效性。

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