Next Article in Journal
Determination of Chewing Count from Video Recordings Using Discrete Wavelet Decomposition and Low Pass Filtration
Previous Article in Journal
Assessment of SMA Electrical Resistance Change during Cyclic Stretching with Small Elongation
Previous Article in Special Issue
Underground Parking Lot Navigation System Using Long-Term Evolution Signal
Article

Lane Detection Aided Online Dead Reckoning for GNSS Denied Environments

1
Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
2
The Robotics Program, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
3
School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Taikjin Lee
Sensors 2021, 21(20), 6805; https://doi.org/10.3390/s21206805
Received: 19 September 2021 / Revised: 6 October 2021 / Accepted: 9 October 2021 / Published: 13 October 2021
(This article belongs to the Special Issue Learning Technology Based on Navigation Sensors)
With the emerging interest of autonomous vehicles (AV), the performance and reliability of the land vehicle navigation are also becoming important. Generally, the navigation system for passenger car has been heavily relied on the existing Global Navigation Satellite System (GNSS) in recent decades. However, there are many cases in real world driving where the satellite signals are challenged; for example, urban streets with buildings, tunnels, or even underpasses. In this paper, we propose a novel method for simultaneous vehicle dead reckoning, based on the lane detection model in GNSS-denied situations. The proposed method fuses the Inertial Navigation System (INS) with learning-based lane detection model to estimate the global position of vehicle, and effectively bounds the error drift compared to standalone INS. The integration of INS and lane model is accomplished by UKF to minimize linearization errors and computing time. The proposed method is evaluated through the real-vehicle experiments on highway driving, and the comparative discussions for other dead-reckoning algorithms with the same system configuration are presented. View Full-Text
Keywords: dead reckoning; lane detection; sensor fusion; multimodal system dead reckoning; lane detection; sensor fusion; multimodal system
Show Figures

Figure 1

MDPI and ACS Style

Jeon, J.; Hwang, Y.; Jeong, Y.; Park, S.; Kweon, I.S.; Choi, S.B. Lane Detection Aided Online Dead Reckoning for GNSS Denied Environments. Sensors 2021, 21, 6805. https://doi.org/10.3390/s21206805

AMA Style

Jeon J, Hwang Y, Jeong Y, Park S, Kweon IS, Choi SB. Lane Detection Aided Online Dead Reckoning for GNSS Denied Environments. Sensors. 2021; 21(20):6805. https://doi.org/10.3390/s21206805

Chicago/Turabian Style

Jeon, Jinhwan, Yoonjin Hwang, Yongseop Jeong, Sangdon Park, In S. Kweon, and Seibum B. Choi 2021. "Lane Detection Aided Online Dead Reckoning for GNSS Denied Environments" Sensors 21, no. 20: 6805. https://doi.org/10.3390/s21206805

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop