This work addresses visual cross-view metric localization for outdoor robotics. Given a ground-level color image and a satellite patch that contains the local surroundings, the task is to identify the location of the ground camera within the satellite patch. Related work addressed this task for range-sensors (LiDAR, Radar), but for vision, only as a secondary regression step after an initial cross-view image retrieval step. Since the local satellite patch could also be retrieved through any rough localization prior (e.g. from GPS/GNSS, temporal filtering), we drop the image retrieval objective and focus on the metric localization only. We devise a novel network architecture with denser satellite descriptors, similarity matching at the bottleneck (rather than at the output as in image retrieval), and a dense spatial distribution as output to capture multi-modal localization ambiguities. We compare against a state-of-the-art regression baseline that uses global image descriptors. Quantitative and qualitative experimental results on the recently proposed VIGOR and the Oxford RobotCar datasets validate our design. The produced probabilities are correlated with localization accuracy, and can even be used to roughly estimate the ground camera’s heading when its orientation is unknown. Overall, our method reduces the median metric localization error by 51%, 37%, and 28% compared to the state-of-the-art when generalizing respectively in the same area, across areas, and across time.

Cross-view matching aims to learn a shared image representation between ground-level images and satellite or aerial images at the same locations. In robotic vehicles, matching a camera image to a database of geo-referenced aerial imagery can serve as a method for self-localization. However, existing work on cross-view matching only aims at global localization, and overlooks the easily accessible rough location estimates from GNSS or temporal filtering. We argue that the availability of coarse location estimates at test time should already be considered during training. We adopt a simple but effective adaptation to the common triplet loss, resulting in an image representation that is more discriminative within the geographically local neighborhood, without any modifications to a baseline deep neural network. Experiments on the CVACT dataset confirm that the improvements generalize across spatial regions. On a new benchmark constructed from the Oxford RobotCar dataset, we also show generalization across recording days within the same region. Finally, we validate that improvements on these image-retrieval benchmarks also translate to a real-world localization task. Using a particle filter to fuse the cross-view matching scores of a vehicle's camera stream with real GPS measurements, our learned geographically local representation reduces the mean localization error by 17\% compared to the standard global representation learned by the current state-of-the-art.