The problem of localization on a geo-referenced satellite map given a query ground view image is useful yet remains challenging due to the drastic change in viewpoint. To this end, in this paper we work on the extension of our earlier work on the Cross-View Matching Network (CVM-Net) for the ground-to-aerial image matching task since the traditional image descriptors fail due to the drastic viewpoint change. In particular, we show more extensive experimental results and analyses of the network architecture on our CVM-Net. Furthermore, we propose a Markov localization framework that enforces the temporal consistency between image frames to enhance the geo-localization results in the case where a video stream of ground view images is available. Experimental results show that our proposed Markov localization framework can continuously localize the vehicle within a small error on our Singapore dataset.
In the early stage, traditional features that were commonly used in the computer vision community were utilized to do the cross-view image matching ( @cite_35 ; @cite_18 ; @cite_45 ; @cite_4 ; @cite_36 ; @cite_12 ). However, due to the huge difference in viewpoint, the aerial image and ground view image of the same location appeared to be very different. This caused direct matching with traditional local features to fail. Hence, a number of approaches warped the ground image to the top-down view to improve feature matching ( @cite_35 ; @cite_45 ; @cite_12 ). In cases where building facades are visible from oblique aerial images, geo-localization can be achieved with facade patch-matching @cite_18 .
@cite_35 @cite_18 @cite_4 @cite_36 @cite_45 @cite_12
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The recent availability of geo-tagged images and rich geospatial data has inspired a number of algorithms for image based geolocalization. Most approaches predict the location of a query image by matching to ground-level images with known locations (e.g., street-view data). However, most of the Earth does not have ground-level reference photos available. Fortunately, more complete coverage is provided by oblique aerial or “bird's eye” imagery. In this work, we localize a ground-level query image by matching it to a reference database of aerial imagery. We use publicly available data to build a dataset of 78K aligned crossview image pairs. The primary challenge for this task is that traditional computer vision approaches cannot handle the wide baseline and appearance variation of these cross-view pairs. We use our dataset to learn a feature representation in which matching views are near one another and mismatched views are far apart. Our proposed approach, Where-CNN, is inspired by deep learning success in face verification and achieves significant improvements over traditional hand-crafted features and existing deep features learned from other large-scale databases. We show the effectiveness of Where-CNN in finding matches between street view and aerial view imagery and demonstrate the ability of our learned features to generalize to novel locations. We study the feasibility of solving the challenging problem of geolocalizing ground level images in urban areas with respect to a database of images captured from the air such as satellite and oblique aerial images. We observe that comprehensive aerial image databases are widely available while complete coverage of urban areas from the ground is at best spotty. As a result, localization of ground level imagery with respect to aerial collections is a technically important and practically significant problem. We exploit two key insights: (1) satellite image to oblique aerial image correspondences are used to extract building facades, and (2) building facades are matched between oblique aerial and ground images for geo-localization. Key contributions include: (1) A novel method for extracting building facades using building outlines; (2) Correspondence of building facades between oblique aerial and ground images without direct matching; and (3) Position and orientation estimation of ground images. We show results of ground image localization in a dense urban area. We address the problem of geo-registering ground-based multi-view stereo models by ground-to-aerial image matching. The main contribution is a fully automated geo-registration pipeline with a novel viewpoint-dependent matching method that handles ground to aerial viewpoint variation. We conduct large-scale experiments which consist of many popular outdoor landmarks in Rome. The proposed approach demonstrates a high success rate for the task, and dramatically outperforms state-of-the-art techniques, yielding geo-registration at pixel-level accuracy. In this paper we present a prototype system for image based localization in urban environments. Given a database of views of city street scenes tagged by GPS locations, the system computes the GPS location of a novel query view. We first use a wide-baseline matching technique based on SIFT features to select the closest views in the database. Often due to a large change of viewpoint and presence of repetitive structures, a large percentage of matches (> 50 ) are not correct correspondences. The subsequent motion estimation between the query view and the reference view, is then handled by a novel and efficient robust estimation technique capable of dealing with large percentage of outliers. This stage is also accompanied by a model selection step among the fundamental matrix and the homography. Once the motion between the closest reference views is estimated, the location of the query view is then obtained by triangulation of translation directions. Approximate solutions for cases when triangulation cannot be obtained reliably are also described. The presented system is tested on the dataset used in ICCV 2005 Computer Vision Contest and is shown to have higher accuracy than previous reported results.