Research and Performance Analysis of Tightly Coupled Vision, INS and GNSS System for Land Vehicle Applications

Abstract

Global navigation satellite system (GNSS) is widely used for positioning and navigation. However, positioning accuracy of standalone GNSS system is badly affected in poor GNSS signal environments. Tightly coupled INS/GNSS has been studied and used to improve the positioning accuracy in poor GNSS signal environments. However, if GNSS signals are unavailable for long period of time, INS is unable to bound the sensor errors and positioning accuracy independently. Vision/IMU coupling is sued for indoor positioning in robots and provides positioning information in local coordinates. This paper is a research on the performance of tightly coupled vision, INS and GNSS system in environments when GNSS signals are good, poor or completely lost. Vision and IMU sensors are tightly coupled. IMU measurements are used to predict the INS navigation parameters and image features are used for measurement update. In tightly coupled Vision/INS system, navigation parameters are used to bound sensor errors. Predicted and updated INS navigation parameters are used in tightly coupled INS/GNSS system architecture to make further corrections in the INS predicted navigation parameters. In this paper we used Extended Kalman Filter (EKF) for integration. Positioning results of Vision/INS aided GNSS integration are compared against GNSS only, Vision/INS and GNSS/INS positioning results.