Autonomous Navigation of Unmanned Aerial Vehicles Based on Multi-Sensor Data Fusion

During the development of Unmanned Aerial Vehicles (UAVs), one of the major concerns has been the issue of improving the accuracy, coverage, and reliability of automaticnavigation system within the imposed weight and cost limitations. Standard aerial navigation systems often rely on Global Positioning System (GPS) and Inertial MeasurementUnit (IMU), alone or in a combination. In aerial vehicles the GPS signal can becomes unreliable, blocked or jammed byinternational interferences (especially for a GPS operating on civilian frequencies). On the other hand, a stand-alone IMUdrifts with time and will be unacceptable after a few seconds (especially for small-size aerial vehicles which use low-cost IMU). In this respect, many researches have been made toimprove of the efficiency and robustness of GPS/IMU navigation by visual aiding; this can be achieved by combininginertial measurements from an IMU with the position resulting from visual observations. This paper represents a method for multi-sensor based navigation of aerial vehicles which is todetermine precise pose parameters of the vehicle in real time. In this context, a Vision-Based Navigation (VBN) system providesattitude and position observations in an Extended Kalman Filter (EKF) algorithm for precisely determining the poseparameters of the vehicle using IMU motion model. The pose estimation strategy has been tested on a number of different sites and experimental results prove the feasibility androbustness of the proposed method

Keywords:

Autonomous Outdoor Navigation System , Extended Kalman Filter , Image Geo-referencing , Multi-Sensor Data Fusion , Strapdown Inertial Navigation , Vision-Based Navigation