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Open AccessArticle

A Panoramic Localizer Based on Coarse-to-Fine Descriptors for Navigation Assistance

State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
National Engineering Research Center of Optical Instrumentation, Zhejiang University, Hangzhou 310058, China
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4177;
Received: 14 June 2020 / Revised: 22 July 2020 / Accepted: 23 July 2020 / Published: 27 July 2020
(This article belongs to the Section Intelligent Sensors)
Visual Place Recognition (VPR) addresses visual instance retrieval tasks against discrepant scenes and gives precise localization. During a traverse, the captured images (query images) would be traced back to the already existing positions in the database images, rendering vehicles or pedestrian navigation devices distinguish ambient environments. Unfortunately, diverse appearance variations can bring about huge challenges for VPR, such as illumination changing, viewpoint varying, seasonal cycling, disparate traverses (forward and backward), and so on. In addition, the majority of current VPR algorithms are designed for forward-facing images, which can only provide with narrow Field of View (FoV) and come with severe viewpoint influences. In this paper, we propose a panoramic localizer, which is based on coarse-to-fine descriptors, leveraging panoramas for omnidirectional perception and sufficient FoV up to 360. We adopt NetVLAD descriptors in the coarse matching in a panorama-to-panorama way, for their robust performances in distinguishing different appearances, utilizing Geodesc keypoint descriptors in the fine stage in the meantime, for their capacity of detecting detailed information, formatting powerful coarse-to-fine descriptors. A comprehensive set of experiments is conducted on several datasets including both public benchmarks and our real-world campus scenes. Our system is proved to be with high recall and strong generalization capacity across various appearances. The proposed panoramic localizer can be integrated into mobile navigation devices, available for a variety of localization application scenarios. View Full-Text
Keywords: visual place recognition; coarse-to-fine descriptors; panoramas; navigation assistance visual place recognition; coarse-to-fine descriptors; panoramas; navigation assistance
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Fang, Y.; Yang, K.; Cheng, R.; Sun, L.; Wang, K. A Panoramic Localizer Based on Coarse-to-Fine Descriptors for Navigation Assistance. Sensors 2020, 20, 4177.

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