Statistical indoor localization using fusion of depth-images and step detection

Author(s):  
Toni Fetzer ◽  
Frank Deinzer ◽  
Lukas Koping ◽  
Marcin Grzegorzek
2021 ◽  
Author(s):  
Kento Yamamoto ◽  
Hideaki Kawano ◽  
Keishiro Kudo ◽  
Kohsuke Yanagihara ◽  
Noboru Nemoto ◽  
...  

2009 ◽  
Vol 14 (2) ◽  
pp. 253-263 ◽  
Author(s):  
Chunwang Gao ◽  
Zhen Yu ◽  
Yawen Wei ◽  
Steve Russell ◽  
Yong Guan

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shuang Li ◽  
Baoguo Yu ◽  
Yi Jin ◽  
Lu Huang ◽  
Heng Zhang ◽  
...  

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.


Author(s):  
Nadia Ghariani ◽  
Mohamed Salah Karoui ◽  
Mondher Chaoui ◽  
Mongi Lahiani ◽  
Hamadi Ghariani

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