Image-based localization plays an important role in today's autonomous driving technologies. However, in large scale outdoor environments, challenging conditions, e.g., lighting changes or different weather, heavily affect image appearance and quality. As a key component of feature-based visual localization, image feature detection and matching deteriorate severely and cause worse localization performance. In this paper, we propose a novel method for robust image feature matching under drastically changing outdoor environments. In contrast to existing approaches which try to learn robust feature descriptors, we train a deep network that outputs the low-rank representations of the images where the undesired variations on the images are removed, and perform feature extraction and matching on the learned low-rank space. We demonstrate that our learned low-rank images largely improve the performance of image feature matching under varying conditions over a long period of time.
Learning Low-Rank Images for Robust All-Day Feature Matching
2019-06-01
2658898 byte
Conference paper
Electronic Resource
English
Robust Point Feature Matching in Projective Space
British Library Conference Proceedings | 2001
|An Efficient Algorithm for Robust Feature Matching
British Library Conference Proceedings | 1998
|ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images
British Library Online Contents | 2016
|Robust point feature matching in projective space
IEEE | 2001
|