Knowledge Commons of Institute of Automation,CAS
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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ICIP2019.pdf(611KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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