Sequence Matching with Discriminative Binary Features for Robust and Fast Light-Rail Localization at High Frame Rate

As an essential part of the advanced driving assistant system, visual localization technology has drawn much attention. To solve the interference caused by the high similarity of scenes at a high frame rate, this paper proposes a localization method based on global-local features and keyframe retrieval. In this method, the semantic features of the scene extracted by the fused semantic segmentation model are used to guide the detection of the key regions with discriminative information. By using the screening strategy based on pixel position cues and unsupervised learning algorithm, the binary feature descriptor with strong discrimination power is extracted, which can improve the matching accuracy and reduce the computational complexity. Secondly, combining the keyframes obtained by calculating scene discriminative score with a sequence matching algorithm can promote localization performance. The experiments conducted on the Nordland dataset and the challenging Hong Kong light rail dataset. The experiment results show that our method has higher computing efficiency and locating accuracy than the state-of-the-art ConvNet featurebased localization method named SeqCNNSLAM under the condition of drastic changes in appearance. Moreover, compared with SeqSLAM which is based on global features, the precision of our approach has significant improvement while meeting real-time requirements.