This article presents an approach to the optical flow (OF) usage as a general navigation means providing the information about the linear and angular vehicle's velocities. The term of “OF” came from opto-electronic devices where it corresponds to a video sequence of images related to the camera motion either over static surfaces or set of objects. Even if the positions of these objects are unknown in advance, one can estimate the camera motion provided just by video sequence itself and some metric information, such as distance between the objects or the range to the surface. This approach is applicable to any passive observation system which is able to produce a sequence of images, such as radio locator or sonar. Here the UAV application of the OF is considered since it is historically
In recent years navigation on the basis of computation of the camera path and the distance to obstacles with the aid of field of image motion velocities (i.e. optical flow, OF) became highly demanded particularly in the area of relatively small and even micro unmanned aerial vehicles (UAV). Video sequences captured by onboard camera gives the possibility of the OF calculation with the aid of relatively simple algorithms like Lucas-Kanade. The complete OF is the linear function of linear and angular velocities of the UAV which provides an additional means for the navigation parameters estimation. Such UAV navigation approach presumes that on-board camera gives the video sequence of the underlying surface images providing the information about the UAV evolutions. Navigation parameters are extracted on the basis of exact OF formulas which gives the observation process description for estimation based on Kalman filtering. One can expect the high accuracy of the estimated parameters (linear and angular velocities) because their number is substantially less than the number of measurements (practically the number of the camera pixels).
This work considers the tracking of the UAV (unmanned aviation vehicle) on the basis of onboard observations of natural landmarks including azimuth and elevation angles. It is assumed that UAV's cameras are able to capture the angular position of reference points and to measure the angles of the sight line. Such measurements involve the real position of UAV in implicit form, and therefore some of nonlinear filters such as Extended Kalman filter (EKF) or others must be used in order to implement these measurements for UAV control. Recently it was shown that modified pseudomeasurement method may be used to control UAV on the basis of the observation of reference points assigned along the UAV path in advance. However, the use of such set of points needs the cumbersome recognition procedure with the huge volume of on-board memory. The natural landmarks serving as such reference points which may be determined on-line can significantly reduce the on-board memory and the computational difficulties. The principal difference of this work is the usage of the 3D reference points coordinates which permits to determine the position of the UAV more precisely and thereby to guide along the path with higher accuracy which is extremely important for successful performance of the autonomous missions. The article suggests the new RANSAC for ISOMETRY algorithm and the use of recently developed estimation and control algorithms for tracking of given reference path under external perturbation and noised angular measurements.
This work considers the tracking of the UAV (unmanned aviation vehicle) at landing on unprepared field. Despite the advantages in UAV guidance the autonomous landing remains to be one of most serious problems. The principal difficulties are the absence of the precise UAV position measurements with respect to the landing field and the action of external atmospheric perturbations (turbulence and wind). So the control problem for UAV landing is the nonlinear stochastic one with incomplete information. The aim of the article is the development of stochastic control algorithms based on pseudomeasurement Kalman filter in the problem of the UAV autonomous landing with the aid of ground-based optical/radio radars in the case of strong wind and large initial error of the UAV entrance into the area covered by radars. The novelty of the article is the joint control-observation algorithm based on unbiased pseudomeasurement Kalman filter which provides the quadratic characteristics of the estimation errors. The later property is highly important for the UAV control based on the data fusion from INS (inertial navigation system) and the bearing observations obtained from external terrain based locators. The principal difficulty in the UAV landing control is the absence of the direct control tools at the terrain end, so the possible control can be based on the angular-range data obtained by terrain locators which must be transmitted from terrain location station to the UAV control unit. Thus the stochastic approach looks very effective in this challenging problem of the UAV landing.