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首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >Diversified shared latent structure based localization for blind persons
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Diversified shared latent structure based localization for blind persons

机译：基于多元化共享潜在结构的盲人定位

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摘要

Indoor localization systems for blind person aims to help visually impaired people localize themselves in indoor environments. Most approaches employ the RGBD camera and LIDAR for accurate localization, yet these devices are not cheap and portable for blind persons. Instead, WiFi signals are quite ubiquitous in most indoor areas, like shopping mall, hospital etc. Therefore, we propose a diversified shared latent variable model that exploits the availability of WiFi for localization. More specifically, the observation spaces in our model, WiFi strength measurements and their corresponding locations, share a single and reduced dimensionality latent space. By building and incorporating a kernel based diversity prior, the learned latent variables are inclined to extract more features of the WiFi signals, such as the coverage area, and thus further enhance the accuracy of localization. The experimental results illustrate our proposed model is accurate and efficient for indoor localization issue.
机译：盲人室内定位系统旨在帮助视障人士在室内环境中定位自己。大多数方法都使用RGBD摄像机和LIDAR进行精确定位，但是这些设备对于盲人而言并不便宜且便携。取而代之的是，WiFi信号在大多数室内区域（如购物商场，医院等）都非常普遍。因此，我们提出了一种多样化的共享潜变量模型，该模型利用WiFi进行本地化。更具体地说，我们模型中的观测空间，WiFi强度测量及其相应位置共享一个单一的降维潜在空间。通过事先构建和合并基于内核的分集，学习到的潜在变量倾向于提取WiFi信号的更多特征（例如覆盖区域），从而进一步提高定位的准确性。实验结果表明，我们提出的模型对于室内定位问题是准确有效的。

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