CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Scene-unified image translation for visual localization
Sheng Han1,2; Wei Gao1,2; Yiming Wan1,2; Yihong Wu1,2
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 key technology in the field of 3D robot vision. One of its major difficulties lies in how to deal with the challenges brought by the appearance changes of query images and database images caused by large time spans. Many methods focus on extracting more robust features from images to deal with the impact of complex scenes. In this paper, we explore the impact of image translation on visual localization tasks in complex scenes. We propose UniGAN - a modified image translation model, fusing semantic label constraints and finer reconstruction losses, to unify images captured under different environmental conditions to a standard scene more suitable for localization tasks. To estimate the 6-DOF camera pose, a two-stage localization framework composed of image retrieval and local matching is utilized. Experiments show that our method outperforms the state-of-the-art in terms of both accuracy and robustness to environmentally sensitive scenes.

Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40601
Collection模式识别国家重点实验室_机器人视觉
Corresponding AuthorSheng Han
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
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Sheng Han,Wei Gao,Yiming Wan,et al. Scene-unified image translation for visual localization[C],2020.
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