A GNSS/IMU/WSS/VSLAM Hybridization Using an Extended Kalman Filter

Accurate positioning is nowadays a pre-requisite in many fields of applications. In Civil Aviation, accurate positioning is required for aircraft in precision approach or for vehicles moving on airport surfaces in order to guarantee their safety. Most of current positioning systems rely on the Global Navigation and Satellite System (GNSS). However, in constrained or semi-constrained environment such as airports, stand-alone GNSS navigation is quite vulnerable since the provided GNSS solution may be degraded by multipath resulting from the diffraction of GNSS signals on the airport obstacles, or even unavailable. A possible solution is to fuse information from different sensors in order to enhance the system performance. Since the targeted application is cost-sensitive, low-cost sensors will be used in this study. Therefore, the challenge of this paper is to achieve a high level of positioning accuracy using a low-cost solution. In order to achieve our objective, a hybridization system fusing information from a GNSS receiver, an Inertial Navigation System (INS), a monocular camera and a Wheel Speed Sensor (WSS) is presented, and a loosely-coupled architecture based on an error-state Extended Kalman Filter (EKF) is proposed. Visual information is processed using the keyframe-based Visual Simultaneous Localization And Mapping (VSLAM) technique. The proposed architecture is centered on the INS and uses measurements from the other sensors. These aiding measurements are output by each navigation system after being processed independently. This paper focuses on the visual module and highlights its contribution to the enhancement of the navigation system performance.