论文标题
GPU加速层次全景图像检索室内定位
GPU-accelerated Hierarchical Panoramic Image Feature Retrieval for Indoor Localization
论文作者
论文摘要
室内本地化有许多应用程序，例如基于商业位置的服务（LB），机器人导航和盲人辅助导航。本文通过使用全景图像特征对视觉地标进行建模，并通过GPU加速并行检索算法来计算用户的位置，从而将室内定位问题提出到多媒体检索问题。为了解决场景相似性问题，我们采用基于多图像的检索策略和2D聚合方法来估计最终检索位置。在校园建造实际数据的实验表明了实时响应（14FPS）和稳健的本地化。
Indoor localization has many applications, such as commercial Location Based Services (LBS), robotic navigation, and assistive navigation for the blind. This paper formulates the indoor localization problem into a multimedia retrieving problem by modeling visual landmarks with a panoramic image feature, and calculating a user's location via GPU- accelerated parallel retrieving algorithm. To solve the scene similarity problem, we apply a multi-images based retrieval strategy and a 2D aggregation method to estimate the final retrieval location. Experiments on a campus building real data demonstrate real-time responses (14fps) and robust localization.
