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BOOSTING IMAGE-BASED LOCALIZATION VIA RANDOMLY GEOMETRIC DATA AUGMENTATION
Wan YM(万一鸣)1,2; Gao W(高伟)1,2; Han S(韩胜)1,2; Wu YH(吴毅红)1,2
2020-10
Conference NameInternational Conference on Image Processing
Conference Date2020.10.25-2020.10.28
Conference PlaceAbu Dhabi, United Arab Emirates
Abstract

Visual localization is a fundamental problem in computer vision
and robotics. Recently, deep learning has shown to be effective
for robust monocular localization. Most deep learningbased
methods utilize convolution neural network (CNN) to
regress global 6 degree-of-freedom (Dof) pose. However,
these methods suffer from pose sparsity, leading to over-fitting
during training and poor localization performance on unseen
data. In this paper, we try to alleviate this issue by implementing
randomly geometric augmentation (RGA) during training.
Specifically, we firstly estimate the depth map using a depth
estimation network for the initial training image. Combing
the estimated depth, RGB image and its corresponding pose,
we can randomly synthesize new images of different views.
The synthesized and initial images are used to train the pose
regression network. Experiment results show our geometric
augmentation strategy can significantly improve the localization
accuracy.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39159
Collection模式识别国家重点实验室_机器人视觉
Affiliation1.中国科学院自动化研究所
2.中国科学院大学
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wan YM,Gao W,Han S,et al. BOOSTING IMAGE-BASED LOCALIZATION VIA RANDOMLY GEOMETRIC DATA AUGMENTATION[C],2020.
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