论文标题
评估跨视图匹配以通过空中感知改善地面车辆定位
Evaluation of Cross-View Matching to Improve Ground Vehicle Localization with Aerial Perception
论文作者
论文摘要
跨视图匹配是指从空中图像数据库中找到给定查询地面图像的最接近的匹配的问题。如果地理位置图像，则可以使用最接近匹配的空中图像来定位查询地面视图图像。由于深度学习方法最近的成功，已经提出了几种跨视图匹配技术。这些方法对于隔离查询图像的匹配表现良好。但是，他们对轨迹的评估是有限的。在本文中，我们评估了跨视图匹配，以在较长的轨迹上定位地面车辆的任务。我们将这些横向匹配视为使用粒子滤波器融合的传感器测量值。我们使用在影片仿真中收集的全市范围数据集评估了该方法的性能：通过改变四个参数：航空图像的高度，空中摄像头安装座的音高，地面摄像头的FOV以及粒子滤波器中融合的跨视图测量方法。我们还报告了使用Google Street View和Satellite View API收集的实际数据集上使用管道获得的结果。
Cross-view matching refers to the problem of finding the closest match for a given query ground view image to one from a database of aerial images. If the aerial images are geotagged, then the closest matching aerial image can be used to localize the query ground view image. Due to the recent success of deep learning methods, several cross-view matching techniques have been proposed. These approaches perform well for the matching of isolated query images. However, their evaluation over a trajectory is limited. In this paper, we evaluate cross-view matching for the task of localizing a ground vehicle over a longer trajectory. We treat these cross-view matches as sensor measurements that are fused using a particle filter. We evaluate the performance of this method using a city-wide dataset collected in a photorealistic simulation by varying four parameters: height of aerial images, the pitch of the aerial camera mount, FOV of the ground camera, and the methodology of fusing cross-view measurements in the particle filter. We also report the results obtained using our pipeline on a real-world dataset collected using Google Street View and satellite view APIs.
