Indoor localization of vehicles using Deep Learning

Abstract

Modern vehicles are equipped with numerous driver assistance and telematics functions, such as Turn-by-Turn navigation. Most of these systems rely on precise positioning of the vehicle. While Global Navigation Satellite Systems (GNSS) are available outdoors, these systems fail in indoor environments such as a car-park or a tunnel. Alternatively, the vehicle can localize itself with landmark-based positioning and internal car sensors, yet this is not only costly but also requires precise knowledge of the enclosed area. Instead, our approach is to use infrastructure-based positioning. Here, we utilize off-the shelf cameras mounted in the car-park and Vehicle-to-Infrastructure Communication to allow all vehicles to obtain an indoor position given from an infrastructure-based localization service. Our approach uses a Convolutional Neural Network (CNN) with Deep Learning to identify and localize vehicles in a car-park. We thus enable position-based Driver Assistance Systems (DAS) and telematics in an underground facility. We compare the novel Deep Learning classifier to a conventional classifier using Haar-like features.

Original document

The different versions of the original document can be found in:

• http://publica.fraunhofer.de/documents/N-421430.html

• http://xplorestaging.ieee.org/ielx7/7518125/7523483/07523569.pdf?arnumber=7523569,

http://dx.doi.org/10.1109/wowmom.2016.7523569

• https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7523569,

https://dblp.uni-trier.de/db/conf/wowmom/wowmom2016.html#KumarSBSR16,

https://academic.microsoft.com/#/detail/2488197043