Place recognition based visual localization in changing environments

Abstract : In many applications, it is crucial that a robot or vehicle localizes itself within the world especially for autonomous navigation and driving. The goal of this thesis is to improve place recognition performance for visual localization in changing environment. The approach is as follows: in off-line phase, geo-referenced images of each location are acquired, features are extracted and saved. While in the on-line phase, the vehicle localizes itself by identifying a previously-visited location through image or sequence retrieving. However, visual localization is challenging due to drastic appearance and illumination changes caused by weather conditions or seasonal changing. This thesis addresses the challenge of improving place recognition techniques through strengthen the ability of place describing and recognizing. Several approaches are proposed in this thesis:1) Multi-feature combination of CSLBP (extracted from gray-scale image and disparity map) and HOG features is used for visual localization. By taking the advantages of depth, texture and shape information, visual recognition performance can be improved. In addition, local sensitive hashing method (LSH) is used to speed up the process of place recognition;2) Visual localization across seasons is proposed based on sequence matching and feature combination of GIST and CSLBP. Matching places by considering sequences and feature combination denotes high robustness to extreme perceptual changes;3) All-environment visual localization is proposed based on automatic learned Convolutional Network (ConvNet) features and localized sequence matching. To speed up the computational efficiency, LSH is taken to achieve real-time visual localization with minimal accuracy degradation.
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Theses
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https://tel.archives-ouvertes.fr/tel-01870520
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Submitted on : Friday, September 7, 2018 - 5:14:07 PM
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  • HAL Id : tel-01870520, version 1

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Yongliang Qiao. Place recognition based visual localization in changing environments. Automatic. Université Bourgogne Franche-Comté, 2017. English. ⟨NNT : 2017UBFCA004⟩. ⟨tel-01870520⟩

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