Abstract

In the typical Inertial Navigation System (INS)/ Global Navigation Satellite System (GNSS) setup for ground vehicle navigation, measures should be taken to maintain the performance when there are GNSS signal outages. Usually, aiding sensors are utilized to reduce the INS drift. A full motion constraint model is developed allowing the online calibration of INS frame with respect to (w.r.t) the motion frame. To obtain better heading and lateral positioning performance, we propose to use of vanishing point (VP) observations of parallel lane markings from a single forward-looking camera to aid the INS. In the VP module, the relative attitude of the camera w.r.t the road frame is derived from the VP coordinates. The state-space model is developed with augmented vertical attitude error state. Finally, the VP module is added to a modified motion constrains module in the Extended Kalman filter (EKF) framework. Simulations and real-world experiments have shown the validity of VP-based method and improved heading and cross-track position accuracy compared with the solution without VP. The proposed method can work jointly with conventional visual odometry to aid INS for better accuracy and robustness.

Highlights

  • Accurate vehicular navigation is of great importance for some core parts in “smart cities”

  • We develop the vanishing point (VP) aiding method based on the idea of Stochastic Cloning Kalman filter [35,36] considering the relative nature of the attitude measurement, which is the estimation tool in the application of Micro Aerial Vehicle indoor navigation [37]

  • The simulation and experiments have shown the validity of using VP of lane markings to mitigate the inertial navigation system (INS) heading error in order to achieve better positioning accuracy

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Summary

Introduction

Accurate vehicular navigation is of great importance for some core parts in “smart cities”. It is used in the Guidance, Navigation and Control systems for autonomous driving, and in V2X (vehicle-to-everything) technologies for effective transportation and cooperative safety communications among vehicles. The inertial navigation system (INS) has the sole capability to produce a complete and continuous set of navigation state data, with high precision during a short time span. The positioning error grows considerably with time, especially when using low-cost MEMS inertial measurement units (IMU). INS should be integrated with other aiding sensors. INS and Global Navigation Satellite System (GNSS) integration is commonly used for outdoor vehicles navigation. There is a possibility of a GNSS receiver being jammed or spoofed [3,4]

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