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A Method for Establishing Correspondences Between Hand-Drawn and Sensor-Generated Maps

机译:建立手绘图和传感器生成图之间对应关系的方法

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Maps, and specifically floor plans, are useful for planning a variety of tasks from arranging furniture to designating conceptual or functional spaces (e.g., kitchen, walkway). However, maps generated directly from robot sensor data can be hard to interpret and use for this purpose, especially for individuals who are not used to them, because of sensor and odometry measurement errors and the probabilistic nature of the mapping algorithms themselves. In this paper, we present an algorithm for quickly laying a floor plan (or other conceptual map) onto a map generated from sensor data, creating a one-to-one mapping between the two This allows humans interacting with the robot to use a more readily-understandable representation of the world, while the robot itself uses the sensor-generated map. We look at two use cases: specifying "no-go" regions within a room, and visually locating objects within a room. Although a user study showed no statistical difference between the two types of maps in terms of performance on this spatial memory task, we argue that floor plans are closer to the mental maps people naturally draw to characterize spaces, and are easier to use for untrained individuals.
机译:地图,尤其是平面图,对于规划从布置家具到指定概念性或功能性空间(例如厨房,人行道)的各种任务很有用。但是,由于传感器和测距法的测量误差以及映射算法本身的概率性,直接从机器人传感器数据生成的地图可能难以解释和用于此目的,特别是对于不习惯使用它们的个人。在本文中,我们提出了一种算法,用于将平面图(或其他概念图)快速放置到由传感器数据生成的地图上,从而在两者之间创建一对一的映射。这使得与机器人进行交互的人类可以使用更多易于理解的世界表示,而机器人本身使用传感器生成的地图。我们来看两个用例:在一个房间内指定“禁止通过”区域,以及在一个房间内可视地定位对象。尽管一项用户研究表明,在执行此空间记忆任务方面,两种类型的地图在统计上没有统计学差异,但我们认为,平面图更接近人们自然描绘的表征空间的心理图,并且对于未经训练的人更易于使用。

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