论文标题
培训基于图像本地化的语义描述符
Training Semantic Descriptors for Image-Based Localization
论文作者
论文摘要
基于视觉的车辆本地化解决方案最近变得流行。我们采用基于图像检索的视觉定位方法。数据库图像保存在GPS坐标中，并且检索到的数据库图像的位置是查询图像的近似位置。我们表明，可以通过仅从语义分段图像中提取的描述符来执行本地化。它是可靠的，尤其是当环境遭受严重照明和季节性变化时。我们的实验表明，语义描述符的本地化性能可以提高到基于RGB图像的最新方法的水平。
Vision based solutions for the localization of vehicles have become popular recently. We employ an image retrieval based visual localization approach. The database images are kept with GPS coordinates and the location of the retrieved database image serves as an approximate position of the query image. We show that localization can be performed via descriptors solely extracted from semantically segmented images. It is reliable especially when the environment is subjected to severe illumination and seasonal changes. Our experiments reveal that the localization performance of a semantic descriptor can increase up to the level of state-of-the-art RGB image based methods.
