Abstract-In this paper a nonlinear observer design for estimation of position, velocity, acceleration, attitude and gyro bias of an Unmanned Aerial Vehicle (UAV) is proposed. The sensor suite consists of an Inertial Measurement Unit (IMU), a Global Navigation Satellite System (GNSS) receiver, a video camera and an altimeter. The camera and machine vision can track features from the environment and calculate the optical flow. These data, together with those from the other sensors, are fed to the observer, that is proven to be uniformly semiglobally exponentially stable (USGES). The performance of the observer is tested on simulated data by assuming that the camera system can provide the necessary information.

No abstract

Abstract-In this paper, exponentially stable non-linear observers for estimation of position, velocity, specific force, attitude and gyro bias of a fixed-wing unmanned aerial vehicle (UAV) are proposed. The sensor suite consists of an Inertial Measurement Unit (IMU), a global navigation satellite system (GNSS) receiver, a camera, an altimeter, and, possibly, auxiliary roll and pitch measurements. A first observer is designed making use of all the named sensors and is proven to be globally exponentially stable (GES). Subsequently, the auxiliary roll and pitch measurements are removed and replaced by an additional feedback loop from the estimated attitude, and the new observer is analysed and proven to be uniformly locally exponentially stable (ULES). An optical flow algorithm is used to calculate the UAV velocity based on the camera images, which is used as a measurement of the body-fixed velocity in the attitude observer. The performance of the observers is tested offline on simulated and experimental data.

This paper presents a vision-aided uniformly semi-globally exponentially stable (USGES) nonlinear observer for estimation of attitude, gyro bias, position, velocity and specific force of a fixed-wing Unmanned Aerial Vehicle (UAV). The nonlinear observer uses measurements from an Inertial Measurement Unit (IMU), a Global Navigation Satellite System (GNSS) receiver, and a video camera. This paper presents a nonlinear observer representation with a computer vision (CV) system without any assumptions related to the distance to objects in the images and the structure of the terrain being recorded. The CV utilizes a monocular camera and the continuous epipolar constraint to calculate body-fixed linear velocity. The observer is named a Continuous Epipolar Optical Flow (CEOF) nonlinear observer. Experimental data from a UAV test flight and simulated data are presented showing that the CEOF nonlinear observer has robust performance. Experimental results are compared with an Extended Kalman Filter (EKF) and illustrate that the estimates of the states converge accurately to the correct values. Results show that using the proposed CV in addition to IMU and GNSS improves the accuracy of the estimates. The CV provides accurate information about the direction of travel of the UAV, which improves the attitude and gyro bias estimate.

No abstract