Abstract
With the increasing demand for location‐based services such as railway stations, airports, and shopping malls, indoor positioning technology has become one of the most attractive research areas. Due to the effects of multipath propagation, wireless‐based indoor localization methods such as WiFi, bluetooth, and pseudolite have difficulty achieving high precision position. In this work, we present an image‐based localization approach which can get the position just by taking a picture of the surrounding environment. This paper proposes a novel approach which classifies different scenes based on deep belief networks and solves the camera position with several spatial reference points extracted from depth images by the perspective‐n‐point algorithm. To evaluate the performance, experiments are conducted on public data and real scenes; the result demonstrates that our approach can achieve submeter positioning accuracy. Compared with other methods, image‐based indoor localization methods do not require infrastructure and have a wide range of applications that include self‐driving, robot navigation, and augmented reality.
Highlights
According to statistics, more than 80 percent of people’s living time is in an indoor environment such as shopping malls, airports, libraries, campuses, and hospitals
We have presented an indoor positioning system based only on cameras
The main work is to use deep learning to identify the category of the scene and use 2D-3D matching feature points to calculate the location
Summary
More than 80 percent of people’s living time is in an indoor environment such as shopping malls, airports, libraries, campuses, and hospitals. The high cost of hardware equipment, construction, and installation as well as maintenance and update is an important factor limiting the development of indoor positioning technology These kinds of methods can only output the position (X, Y, and Z coordinates) but not the view angle (pitch, yaw, and roll angles). The vision-based positioning method is a kind of passive positioning technology which can achieve high positioning accuracy and does not need extra infrastructure It can output the position and the view angle at the same time. The complex three-dimensional shape of the environment results in occlusions, overlaps, shadows, and reflections which require a robust description of the scene [16] To address these issues, Wireless Communications and Mobile Computing we propose a efficient approach based on a deep belief network with local binary pattern feature descriptors. We restrict the search space according to adaptive visibility constraints which allows us to cope with extensive maps
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