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Open AccessArticle

A Low-Cost Underground Garage Navigation Switching Algorithm Based on Kalman Filtering

1
Collage of Automation, Harbin Engineering University, Harbin 150001, China
2
School of Aeronautics Engineering, Harbin Institute of Technology, Harbin 150006, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(8), 1861; https://doi.org/10.3390/s19081861
Received: 4 March 2019 / Revised: 3 April 2019 / Accepted: 11 April 2019 / Published: 18 April 2019
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Abstract

Modern parking lots have gradually developed into underground garages to improve the efficient use of space. However, the complex design of parking lots also increases the demands on vehicle navigation. The traditional method of navigation switching only uses satellite signals. After the Position Dilution Of Precision (PDOP) of satellite signals is over the limit, vehicle navigation will enter indoor mode. It is not suitable for vehicles in underground garages to switch modes with a fast-response system. Therefore, this paper chooses satellite navigation, inertial navigation, and the car system to combine navigation. With the condition that the vehicle can freely travel through indoor and outdoor environments, high-precision outdoor environment navigation is used to provide the initial state of underground navigation. The position of the vehicle underground is calculated by the Dead Reckoning (DR) navigation system. This paper takes advantage of the Extended Kalman Filter (EKF) algorithm to provide two freely switchable navigation modes for vehicles in ground and underground garages. The continuity, robustness, fast response, and low cost of the indoor and outdoor switching navigation methods are verified in real-time systems. View Full-Text
Keywords: parking lot; positioning; indoor and outdoor switching navigation method parking lot; positioning; indoor and outdoor switching navigation method
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Li, N.; Gao, Y.; Wang, Y.; Liu, Z.; Guan, L.; Liu, X. A Low-Cost Underground Garage Navigation Switching Algorithm Based on Kalman Filtering. Sensors 2019, 19, 1861.

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