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Mobile Information Systems
Volume 2017 (2017), Article ID 8104386, 14 pages
Research Article

Efficient ConvNet Feature Extraction with Multiple RoI Pooling for Landmark-Based Visual Localization of Autonomous Vehicles

1College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, China
2Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8

Correspondence should be addressed to Yi Hou; moc.liamg@eiwohuohiy

Received 12 May 2017; Revised 28 July 2017; Accepted 11 October 2017; Published 9 November 2017

Academic Editor: Paolo Bellavista

Copyright © 2017 Yi Hou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Efficient and robust visual localization is important for autonomous vehicles. By achieving impressive localization accuracy under conditions of significant changes, ConvNet landmark-based approach has attracted the attention of people in several research communities including autonomous vehicles. Such an approach relies heavily on the outstanding discrimination power of ConvNet features to match detected landmarks between images. However, a major challenge of this approach is how to extract discriminative ConvNet features efficiently. To address this challenging, inspired by the high efficiency of the region of interest (RoI) pooling layer, we propose a Multiple RoI (MRoI) pooling technique, an enhancement of RoI, and a simple yet efficient ConvNet feature extraction method. Our idea is to leverage MRoI pooling to exploit multilevel and multiresolution information from multiple convolutional layers and then fuse them to improve the discrimination capacity of the final ConvNet features. The main advantages of our method are (a) high computational efficiency for real-time applications; (b) GPU memory efficiency for mobile applications; and (c) use of pretrained model without fine-tuning or retraining for easy implementation. Experimental results on four datasets have demonstrated not only the above advantages but also the high discriminating power of the extracted ConvNet features with state-of-the-art localization accuracy.