TY - JOUR
T1 - Robust IμGPS/VO Integration for Vehicle Navigation in GNSS Degraded Urban Areas
AU - Sun, Rui
AU - Yang, Yuanxi
AU - Chiang, Kai Wei
AU - Duong, Thanh Trung
AU - Lin, Kuan Ying
AU - Tsai, Guang Je
N1 - Funding Information:
Manuscript received March 18, 2020; revised April 6, 2020; accepted April 14, 2020. Date of publication April 21, 2020; date of current version August 5, 2020. This work was supported in part by the sponsorship of the National Natural Science Foundation of China under Grant 41974033 and Grant 41704022, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20170780, in part by the China Postdoctoral Science Foundation Funded Project under Grant 2017M623360, and in part by the “Six Talent Peak” Project of Jiangsu Province under Grant KTHY-014. The associate editor coordinating the review of this article and approving it for publication was Dr. Prosanta Gope. (Corresponding author: Guang-Je Tsai.) Rui Sun is with the State Key Laboratory of Geo-Information Engineering, Xi’an Research Institute of Surveying and Mapping, Xi’an 710054, China, and also with the College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China (e-mail: rui.sun@nuaa.edu.cn).
Publisher Copyright:
© 2001-2012 IEEE.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - Global Navigation Satellite Systems (GNSS) integrated with multiple sensors have been widely applied in many Intelligent Transport Systems (ITS). Intelligent vehicles, increasingly a key component of the future transport system, require high-performance positioning, navigation and timing (PNT) technologies. This cannot be achieved by current 2-dimnsional systems. This is because accurate comprehensive state information (time, position, derivatives and attitude) is required to estimate amongst others, the vehicle's power usage as well as to enable precise environmental mapping based on accurate state of the relevant sensors and path planning including in complex multi-level intersections. Because of the multipath effects and signal interruption in urban environments, comprehensive vehicle state estimation is not always available at the required level of performance. To address this issue, we propose an effective method to integrate the Inertial Measurement Unit (IMU), Global Positioning System (GPS) and monocular Visual Odometry (VO) for urban vehicle navigation. A robust Extended Kalman Filter (EKF) based two-step integration algorithm is developed with a non-holonomic constraint (NHC). In particular, the NHC is not only applied on the offline VO error modelling process, but also on the online sensor fusion process to improve the 3D vehicle state estimation. The proposed IμGPS/VO integration scheme is tested with various sensor levels in different urban environments. The results show that the proposed IμGPS/VO fusion algorithm could deliver a 3D RMSE of 3.285m, which outperforms the other conventional candidate fusion schemes in the noisy GNSS urban areas. The further test in the urban with outages has demonstrated that the proposed algorithm delivers an overall 3D RMSE of 1.290m, 0.073m/s and 0.486 degrees, in terms of positioning, velocity and attitude, respectively. It is also demonstrated that the proposed low-cost VO integration with IμGPS could achieve similar performance with the high-cost odometer based integration in deep urban areas but with advantages of higher flexibility and lower cost.
AB - Global Navigation Satellite Systems (GNSS) integrated with multiple sensors have been widely applied in many Intelligent Transport Systems (ITS). Intelligent vehicles, increasingly a key component of the future transport system, require high-performance positioning, navigation and timing (PNT) technologies. This cannot be achieved by current 2-dimnsional systems. This is because accurate comprehensive state information (time, position, derivatives and attitude) is required to estimate amongst others, the vehicle's power usage as well as to enable precise environmental mapping based on accurate state of the relevant sensors and path planning including in complex multi-level intersections. Because of the multipath effects and signal interruption in urban environments, comprehensive vehicle state estimation is not always available at the required level of performance. To address this issue, we propose an effective method to integrate the Inertial Measurement Unit (IMU), Global Positioning System (GPS) and monocular Visual Odometry (VO) for urban vehicle navigation. A robust Extended Kalman Filter (EKF) based two-step integration algorithm is developed with a non-holonomic constraint (NHC). In particular, the NHC is not only applied on the offline VO error modelling process, but also on the online sensor fusion process to improve the 3D vehicle state estimation. The proposed IμGPS/VO integration scheme is tested with various sensor levels in different urban environments. The results show that the proposed IμGPS/VO fusion algorithm could deliver a 3D RMSE of 3.285m, which outperforms the other conventional candidate fusion schemes in the noisy GNSS urban areas. The further test in the urban with outages has demonstrated that the proposed algorithm delivers an overall 3D RMSE of 1.290m, 0.073m/s and 0.486 degrees, in terms of positioning, velocity and attitude, respectively. It is also demonstrated that the proposed low-cost VO integration with IμGPS could achieve similar performance with the high-cost odometer based integration in deep urban areas but with advantages of higher flexibility and lower cost.
UR - http://www.scopus.com/inward/record.url?scp=85089484896&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089484896&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2020.2989332
DO - 10.1109/JSEN.2020.2989332
M3 - Article
AN - SCOPUS:85089484896
SN - 1530-437X
VL - 20
SP - 10110
EP - 10122
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 17
M1 - 9075286
ER -