Outdoor visual localization is a crucial component to many computer vision systems. We propose an approach to localization from images that is designed to explicitly handle the strong variations in appearance happening between daytime and nighttime. As revealed by recent long-term localization benchmarks, both traditional feature-based and retrieval-based approaches still struggle to handle such changes. Our novel localization method combines a state-of-the-art image retrieval architecture with condition-specific sub-networks allowing the computation of global image descriptors that are explicitly dependent of the capturing conditions. We show that our approach improves localization by a factor of almost 300 compared to the popular VLAD-based methods on nighttime localization.
Another way of making feature-based methods robust to condition perturbations is to use semantic reasoning. Semantic information can indeed be exploited to enhance either the feature matching stage @cite_42 @cite_3 @cite_46 @cite_26 or the pose estimation stage @cite_0 . While being accurate at small scale, feature-based methods bottleneck is scalability. Both the construction of precise 3D models (and their maintenance), and local feature-matching is challenging and expensive in large-scale scenarios @cite_70 .
@cite_26 @cite_70 @cite_42 @cite_3 @cite_0 @cite_46
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Accurate visual localization is a key technology for autonomous navigation. 3D structure-based methods employ 3D models of the scene to estimate the full 6DOF pose of a camera very accurately. However, constructing (and extending) large-scale 3D models is still a significant challenge. In contrast, 2D image retrieval-based methods only require a database of geo-tagged images, which is trivial to construct and to maintain. They are often considered inaccurate since they only approximate the positions of the cameras. Yet, the exact camera pose can theoretically be recovered when enough relevant database images are retrieved. In this paper, we demonstrate experimentally that large-scale 3D models are not strictly necessary for accurate visual localization. We create reference poses for a large and challenging urban dataset. Using these poses, we show that combining image-based methods with local reconstructions results in a pose accuracy similar to the state-of-the-art structure-based methods. Our results suggest that we might want to reconsider the current approach for accurate large-scale localization. Successful large scale object instance retrieval systems are typically based on accurate matching of local descriptors, such as SIFT. However, these local descriptors are often not sufficiently distinctive to prevent false correspondences, as they only consider the gradient appearance of the local patch, without being able to “see the big picture”. Image-to-image feature matching is the single most restrictive time bottleneck in any matching pipeline. We propose two methods for improving the speed and quality by employing semantic scene segmentation. First, we introduce a way of capturing semantic scene context of a key point into a compact description. Second, we propose to learn correct match ability of descriptors from these semantic contexts. Finally, we further reduce the complexity of matching to only a pre-computed set of semantically close key points. All methods can be used independently and in the evaluation we show combinations for maximum speed benefits. Overall, our proposed methods outperform all baselines and provide significant improvements in accuracy and an order of magnitude faster key point matching. Robust and accurate visual localization across large appearance variations due to changes in time of day, seasons, or changes of the environment is a challenging problem which is of importance to application areas such as navigation of autonomous robots. Traditional feature-based methods often struggle in these conditions due to the significant number of erroneous matches between the image and the 3D model. In this paper, we present a method for scoring the individual correspondences by exploiting semantic information about the query image and the scene. In this way, erroneous correspondences tend to get a low semantic consistency score, whereas correct correspondences tend to get a high score. By incorporating this information in a standard localization pipeline, we show that the localization performance can be significantly improved compared to the state-of-the-art, as evaluated on two challenging long-term localization benchmarks. The problem of localization and geo-location estimation of an image has a long-standing history both in robotics and computer vision. With the advent of availability of large amounts of geo-referenced image data, several image retrieval approaches have been deployed to tackle this problem. In this work, we will show how the capability of semantic labeling of both query views and the reference dataset by means of semantic segmentation can aid (1) the problem of retrieval of views similar and possibly overlapping with the query and (2) guide the recognition and discovery of commonly occurring scene layouts in the reference dataset. We will demonstrate the effectiveness of these semantic representations on examples of localization, semantic concept discovery, and intersection recognition in the images of urban scenes.