.png){kind=icon}
返回
A Visual and VAE Based Hierarchical Indoor Localization Method
.png){kind=icon}
OA
AI
DOI：10.3390/s21103406.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Precise localization and pose estimation in indoor environments are commonly employed in a wide range of applications, including robotics, augmented reality, and navigation and positioning services. Such applications can be solved via visual-based localization using a pre-built 3D model. The increase in searching space associated with large scenes can be overcome by retrieving images in advance and subsequently estimating the pose. The majority of current deep learning-based image retrieval methods require labeled data, which increase data annotation costs and complicate the acquisition of data. In this paper, we propose an unsupervised hierarchical indoor localization framework that integrates an unsupervised network variational autoencoder (VAE) with a visual-based Structure-from-Motion (SfM) approach in order to extract global and local features. During the localization process, global features are applied for the image retrieval at the level of the scene map in order to obtain candidate images, and are subsequently used to estimate the pose from 2D-3D matches between query and candidate images. RGB images only are used as the input of the proposed localization system, which is both convenient and challenging. Experimental results reveal that the proposed method can localize images within 0.16 m and 4 degrees in the 7-Scenes data sets and 32.8% within 5 m and 20 degrees in the Baidu data set. Furthermore, our proposed method achieves a higher precision compared to advanced methods.
Keyword:
indoor localization
computer vision (CV)
variational autoencoder (VAE)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Different Forms of Vigilance in Response to the Presence of Predators and Conspecifics in a Group‐Living Mammal, the European Rabbit.png)
Ethology
EthologyIF0
PREAI
分享收藏
查看更多内容