TY - JOUR
T1 - Practical modeling of GNSS for autonomous vehicles in urban environments
AU - Lee, Woosik
AU - Cho, Hyojoo
AU - Hyeong, Seungho
AU - Chung, Woojin
N1 - Funding Information:
Funding: This research was supported by the NRF, MSIP (NRF2017R1A2A1 A17069329).
Funding Information:
This research was supported by the NRF, MSIP (NRF2017R1A2A1 A17069329).
PY - 2019/10/1
Y1 - 2019/10/1
N2 - Autonomous navigation technology is used in various applications, such as agricultural robots and autonomous vehicles. The key technology for autonomous navigation is ego-motion estimation, which uses various sensors. Wheel encoders and global navigation satellite systems (GNSSs) are widely used in localization for autonomous vehicles, and there are a few quantitative strategies for handling the information obtained through their sensors. In many cases, the modeling of uncertainty and sensor fusion depends on the experience of the researchers. In this study, we address the problem of quantitatively modeling uncertainty in the accumulated GNSS and in wheel encoder data accumulated in anonymous urban environments, collected using vehicles. We also address the problem of utilizing that data in ego-motion estimation. There are seven factors that determine the magnitude of the uncertainty of a GNSS sensor. Because it is impossible to measure each of these factors, in this study, the uncertainty of the GNSS sensor is expressed through three variables, and the exact uncertainty is calculated. Using the proposed method, the uncertainty of the sensor is quantitatively modeled and robust localization is performed in a real environment. The approach is validated through experiments in urban environments.
AB - Autonomous navigation technology is used in various applications, such as agricultural robots and autonomous vehicles. The key technology for autonomous navigation is ego-motion estimation, which uses various sensors. Wheel encoders and global navigation satellite systems (GNSSs) are widely used in localization for autonomous vehicles, and there are a few quantitative strategies for handling the information obtained through their sensors. In many cases, the modeling of uncertainty and sensor fusion depends on the experience of the researchers. In this study, we address the problem of quantitatively modeling uncertainty in the accumulated GNSS and in wheel encoder data accumulated in anonymous urban environments, collected using vehicles. We also address the problem of utilizing that data in ego-motion estimation. There are seven factors that determine the magnitude of the uncertainty of a GNSS sensor. Because it is impossible to measure each of these factors, in this study, the uncertainty of the GNSS sensor is expressed through three variables, and the exact uncertainty is calculated. Using the proposed method, the uncertainty of the sensor is quantitatively modeled and robust localization is performed in a real environment. The approach is validated through experiments in urban environments.
KW - EKF localization
KW - GNSS sensor model
KW - Odometry calibration
UR - http://www.scopus.com/inward/record.url?scp=85072779164&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072779164&partnerID=8YFLogxK
U2 - 10.3390/s19194236
DO - 10.3390/s19194236
M3 - Article
C2 - 31569556
AN - SCOPUS:85072779164
VL - 19
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
SN - 1424-8220
IS - 19
M1 - 4236
ER -