Metric localization using Google Street View
Metric localization using Google Street View
复制标题
使用 Google 街景进行指标本地化
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Luciano Spinello
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文献类型：
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作者：
Pratik Agarwal;Wolfram Burgard;Luciano Spinello
精确的测量定位是移动机器人面临的主要挑战之一。现有的许多方法都是在机器人构建地图后进行定位。在这篇文章中，我们提出了一种新的方法，使用谷歌街景的地理标记全景图作为全球定位的来源。我们将定位问题建模为两个阶段的非线性最小二乘估计。第一种方法是从短小的单目摄像机序列中估计被跟踪特征点的3D位置。第二种方法计算街景全景图和估计点之间的刚体变换。这种方法的唯一输入是一系列单目摄像机图像和里程估计。我们通过在停车场的机器人平台上运行该方法来量化该方法的准确性，使用视觉基准作为基本事实。此外，我们通过使用来自Google Tango平板电脑的数据，将该方法应用于真实城市场景中的个人本地化。
Accurate metrical localization is one of the central challenges in mobile robotics. Many existing methods aim at localizing after building a map with the robot. In this paper, we present a novel approach that instead uses geo-tagged panoramas from the Google Street View as a source of global positioning. We model the problem of localization as a non-linear least squares estimation in two phases. The first estimates the 3D position of tracked feature points from short monocular camera sequences. The second computes the rigid body transformation between the Street View panoramas and the estimated points. The only input of this approach is a stream of monocular camera images and odometry estimates. We quantified the accuracy of the method by running the approach on a robotic platform in a parking lot by using visual fiducials as ground truth. Additionally, we applied the approach in the context of personal localization in a real urban scenario by using data from a Google Tango tablet.