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UBC Theses and Dissertations
Learning image-based localization Meng, Lili
Abstract
Image-based localization plays a vital role in many tasks of robotics and computer vision, such as global localization, recovery from tracking failure, and loop closure detection. Recent methods based on regression forests for camera relocalization directly predict 3D world locations for 2D image locations to guide camera pose optimization. During training, each tree greedily splits the samples to minimize the spatial variance. This thesis develops techniques to improve the performance camera pose estimation based on regression forests method and extends its application domains. First, random features and sparse features are combined so that the new method only requires an RGB image in the testing. After that, a label-free sample-balanced objective is developed to encourage equal numbers of samples in the left and right sub-trees, and a novel backtracking scheme is developed to remedy the incorrect 2D-3D correspondence in the leaf nodes caused by greedy splitting. Furthermore, the methods based on regression forests are extended to use local features in both training and test stages for outdoor applications, eliminating their dependence on depth images. Finally, a new camera relocalization method is developed using both points and lines. Experimental results on publicly available indoor and outdoor datasets demonstrate the efficacy of the developed approaches, showing superior or on-par accuracy with several state-of-the-art baselines. Moreover, an integrated software and hardware system is presented for mo- bile robot autonomous navigation in uneven and unstructured indoor environments. This modular and reusable software framework incorporates capabilities of perception and autonomous navigation. The system is evaluated are in both simulation and real-world experiments, demonstrating the efficacy and efficiency of the developed system.
Item Metadata
Title |
Learning image-based localization
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2017
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Description |
Image-based localization plays a vital role in many tasks of robotics and computer
vision, such as global localization, recovery from tracking failure, and loop closure
detection. Recent methods based on regression forests for camera relocalization
directly predict 3D world locations for 2D image locations to guide camera pose
optimization. During training, each tree greedily splits the samples to minimize
the spatial variance. This thesis develops techniques to improve the performance
camera pose estimation based on regression forests method and extends its application domains. First, random features and sparse features are combined so that
the new method only requires an RGB image in the testing. After that, a label-free
sample-balanced objective is developed to encourage equal numbers of samples
in the left and right sub-trees, and a novel backtracking scheme is developed to
remedy the incorrect 2D-3D correspondence in the leaf nodes caused by greedy
splitting. Furthermore, the methods based on regression forests are extended to use
local features in both training and test stages for outdoor applications, eliminating
their dependence on depth images. Finally, a new camera relocalization method is
developed using both points and lines. Experimental results on publicly available
indoor and outdoor datasets demonstrate the efficacy of the developed approaches,
showing superior or on-par accuracy with several state-of-the-art baselines.
Moreover, an integrated software and hardware system is presented for mo-
bile robot autonomous navigation in uneven and unstructured indoor environments.
This modular and reusable software framework incorporates capabilities of perception and autonomous navigation. The system is evaluated are in both simulation
and real-world experiments, demonstrating the efficacy and efficiency of the developed system.
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Genre | |
Type | |
Language |
eng
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Date Available |
2017-09-25
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0355764
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2017-11
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International