利用天花板空间密度实现全局视觉定位

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标题：Vision-based Global Localization using Ceiling Space Density

作者：Arthur Ribacki, Vitor A. M. Jorge, Mathias Mantelli, Renan Maffei and Edson Prestes

来源：IEEE International Conference on Robotics and Automation (ICRA), 2018, Brisbane

编译：林旭滨

审核：颜青松，陈世浪

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摘要

服务机器人走进千家万户已经成为事实，但其应用范围仍然受限于机器人在环境中的定位能力。对于人工环境，可以利用其建筑蓝图作为机器人定位系统的输入信息，提高机器人在家庭服务的智能化水平。另一方面，家居环境中的动态物体以及家具，却极大增加了机器人定位的难度，尤其是对于仅配备前向水平距离传感器(forward-facing horizontal rangefinder)的机器人而言。

本文通过将无障碍空间密度(free space density)概念应用在一个朝向天花板的相机上，实现在家居环境中的有效全局定位。通过利用建筑蓝图信息以及从天花板提取的视觉特征，实现鲁棒的定位，即使是在有遮挡的情况下。

图1 建筑蓝图（a）以及生成的栅格占据地图（b）。

图2 朝向天花板获取的图像示意。

本文以清洁机器人作为实验平台，该平台上配备有一个朝向上方的相机，在两个房间中的多个运动情况进行了实地的实验验证，并与现有的方法做对比。实验结果表明，我们的方法具有更好的定位性能，并且无需对移动物体和家具进行建模。

表 1 实验结果评估，其中CSD表示本文所提出的方法。

Abstract

Service robots are becoming a reality across homes. Still, the range of applications supported by such robots remains tied to their ability to self-localize in the environment. Man-made constructions often have a documented blueprint which can be used as input information for robot localization and smart home applications. However, home environments commonly include movable objects and furniture, which can make localization a complicated task, especially for forward-facing horizontal range-finders. In this paper, we present a new and effective global localization approach for home environments which adapts the notion of free space density to a camera pointing to the ceiling. We exploit the available blueprint information, as well as evidence that ceiling vision can provide robust localization information, even in the presence of occlusions. We perform real-world experiments using a robotic vacuum cleaner equipped with an upward-facing camera in two different apartments across multiple trajectories and compare the proposed method with competing approaches. Our solution shows superior localization results using maps where neither furniture or movable objects are not modeled.