Robust IμGPS/VO Integration for Vehicle Navigation in GNSS Degraded Urban Areas

Rui Sun, Yuanxi Yang, Kai Wei Chiang, Thanh Trung Duong, Kuan Ying Lin, Guang Je Tsai

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number9075286
Pages (from-to)10110-10122
Number of pages13
JournalIEEE Sensors Journal
Volume20
Issue number17
DOIs
Publication statusPublished - 2020 Sep 1

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Electrical and Electronic Engineering

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