Visual Localization Using Sparse Semantic 3D Map
Tianxin Shi; Shuhan Shen; Xiang Gao; Lingjie Zhu
2019
会议名称IEEE International Conference on Image Processing 2019
会议日期2019-9
会议地点Taipei
摘要

Accurate and robust visual localization under a wide range of viewing condition variations including season and illumination changes, as well as weather and day-night variations, is the key component for many computer vision and robotics applications. Under these conditions, most traditional methods would fail to locate the camera. In this paper we present a visual localization algorithm that combines structure-based method and image-based method with semantic information. Given semantic information about the query and database images, the retrieved images are scored according to the semantic consistency of the 3D model and the query image. Then the semantic matching score is used as weight for RANSAC's sampling and the pose is solved by a standard PnP solver. Experiments on the challenging long-term visual localization benchmark dataset demonstrate that our method has significant improvement compared with the state-of-the-arts.

七大方向——子方向分类三维视觉
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/26088
专题多模态人工智能系统全国重点实验室_机器人视觉
作者单位Institute of Automation, Chinese Academy of Sciences
第一作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Tianxin Shi,Shuhan Shen,Xiang Gao,et al. Visual Localization Using Sparse Semantic 3D Map[C],2019.
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