Single Camera Vehicle Localization Using Feature Scale Tracklets
Single Camera Vehicle Localization Using Feature Scale Tracklets
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DOI：
10.1587/transfun.e100.a.702
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发表时间：
2017-02
期刊：
影响因子：
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通讯作者：
D. Wong;Daisuke Deguchi;I. Ide;H. Murase
中科院分区：
文献类型：
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作者：
D. Wong;Daisuke Deguchi;I. Ide;H. Murase
智能车辆系统的进步使现代汽车能够帮助驾驶员完成车道跟随和自动制动等任务。这种自动驾驶任务越来越需要可靠的自我定位。虽然有大量的传感器可以用于此目的，但使用单个相机仍然是最具吸引力的，但也是最具挑战性的。城市环境中的GPS定位对于自动驾驶系统来说可能不够可靠，并且距离传感器和惯性导航系统的各种组合对于消费者设置来说通常过于复杂和昂贵。因此，利用单个相机进行精确定位是期望的目标。在本文中，我们提出了一种方法，车辆定位使用从一个单一的车载摄像头和一个预先构建的数据库捕获的图像。提取图像特征点，但不需要计算相机姿态-相反，我们使用特征点的比例。对于基于图像特征的定位方法，许多特征与候选数据库图像的匹配是耗时的，并且数据库大小可能变得很大。因此，在这里，我们提出了一种方法，构建一个数据库与已知良好的规模稳定性的预匹配功能。这限制了未使用和不正确匹配的特征的数量，并允许将数据库缩放记录到“tracklet”中。这些“特征尺度轨迹”用于基于与相应查询图像特征的尺度比较的快速图像匹配投票。该过程减少了需要执行的图像到图像匹配迭代的次数，同时提高了定位稳定性。我们还提出了一个系统性能的分析，使用具有高精度地面实况的数据集。我们证明了强大的车辆定位，即使在具有挑战性的车道变化和真实的交通情况。关键词：自我定位，单目视觉，特征尺度
Advances in intelligent vehicle systems have led to modern automobiles being able to aid drivers with tasks such as lane following and automatic braking. Such automated driving tasks increasingly require reliable ego-localization. Although there is a large number of sensors that can be employed for this purpose, the use of a single camera still remains one of the most appealing, but also one of the most challenging. GPS localization in urban environments may not be reliable enough for automated driving systems, and various combinations of range sensors and inertial navigation systems are often too complex and expensive for a consumer setup. Therefore accurate localization with a single camera is a desirable goal. In this paper we propose a method for vehicle localization using images captured from a single vehicle-mounted camera and a pre-constructed database. Image feature points are extracted, but the calculation of camera poses is not required — instead we make use of the feature points’ scale. For image feature-based localization methods, matching of many features against candidate database images is time consuming, and database sizes can become large. Therefore, here we propose a method that constructs a database with pre-matched features of known good scale stability. This limits the number of unused and incorrectly matched features, and allows recording of the database scales into “tracklets”. These “Feature scale tracklets” are used for fast image match voting based on scale comparison with corresponding query image features. This process reduces the number of image-to-image matching iterations that need to be performed while improving the localization stability. We also present an analysis of the system performance using a dataset with high accuracy ground truth. We demonstrate robust vehicle positioning even in challenging lane change and real traffic situations. key words: ego-localization, monocular vision, feature scale