Abstract
The aim of image-based localization (IBL) is to localize the real location of query image by matching reference image in database with GNSS-tags. Popular methods related to IBL commonly use street-level images, which have high value in practical application. Using street-level image to tackle IBL task has the primary challenges: existing works have not made targeted optimization for urban IBL tasks. Besides, the matching result is over-reliant on the quality of image features. Methods should address their practicality and robustness in engineering application, under metropolitan-scale. In response to these, this paper made following contributions: firstly, given the critical of buildings in distinguishing urban scenes, we contribute a feature called Building-Aware Feature (BAF). Secondly, in view of negative influence of complex urban scenes in retrieval process, we propose a retrieval method called Patch-Region Retrieval (PRR). To prove the effectiveness of BAF and PRR, we established an image-based localization experimental framework. Experiments prove that BAF can retain the feature points that fall on the building, and selectively lessen the feature points that fall on other things. While this effectively compresses the storage amount of feature index, we can also improve recall of localization results; implemented in the stage of geometric verification, PRR compares matching results of regional features and selects the best ranking as final result. PRR can enhance effectiveness of patch-regional feature. In addition, we fully confirmed the superiority of our proposed methods through a metropolitan-scale street-level image dataset.
Highlights
To correctly locate a street-level image, Image-Based Localization (IBL) task matches the features of image with unknown-location-information and the features of image withGNSS-tags in database
This paper mainly contributes these: (1) we propose Building-Aware Feature as we comprehensively considered the discriminativeness of the building in urban IBL task and the patch-level matching ability that the feature should have
(2) We proposed stage of geometric verification by contribute a patch-region retrieval (PRR) algorithm
Summary
To correctly locate a street-level image, Image-Based Localization (IBL) task matches the features of image with unknown-location-information and the features of image withGNSS-tags in database. With the introduction of image search by image, according to internet search giant, the application of IBL attracted widespread attention. These academic fields own high enthusiasm for researching IBL: object detection [1,2,3], visual localization [4,5,6], simultaneous localization and mapping (SLAM) [7], etc. As a soft-plus [44] activation function on the top of the attention module This method first generates embedding on the entire input image, and the softmax-based classifier is connected. According to nonmaximum suppression [45], retain the feature points with the highest attention score in the same pixel of image. Dimensionality reduction achieves the proper balance between compactness and discriminability
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call