Abstract
The devised approaches are adapted to the complicated conditions of observation in certain real tasks, and are fully operational in those cases when existing standard algorithms fail to give reliable results. We propose a method for determining dynamic motion parameters based on the algorithm of a dense optical flow using a texture analysis. In order to determine an optical flow, we employed a block mapping method that uses adaptively variable size and adaptive motion vector search strategy with weighting the measurements of image blocks, where each block is matched with a texture indicator. A standard block method for estimating optical flow does not imply the use of weighting of the image blocks. A measure of the image block texturization and, consequently, the reliability of the computed motion vector, is determined on the basis of conditionality number of the information matrix. Based on the calculated optical flow, in order to estimate motion parameters, it is proposed to use the least square method that takes into account noise of the measured data. In this case, the minimization is applied at which a contribution to an error is weighed, greater importance is given to the points where the optical flow speed is larger. This is most useful when the measurement of high speeds is more accurate. The norm that produces the best results depends on the noise properties in the measured optical flow. When estimating parameters of the translational motion velocity of the entire image frame, the proposed method considers textural differences of the underlying surface, as well as noise in the measured data of each image block.We presented simulation results of a UAV motion along different types of the underlying surface and estimated the accuracy of determining translational motion parameters using the optical sensor. Experimental results confirm that the application of a texture analysis when evaluating a motion field improves performance by recruiting a reduced number of vectors, as well as this proves to be more accurate in comparison with traditional block brute-force methods
Highlights
Recent years have seen a surge of interest in mini and micro UAVs, associated with the possibilities of low-cost monitoring and for the purposes of remote sensing of the Earth’s surface [1, 2]
We introduce parameter Aij, which is the determinant of data matrix ∇Bij (t + Dt)
In order to study accuracy of the algorithm operation, we developed a software using the MATLAB software
Summary
Recent years have seen a surge of interest in mini and micro UAVs, associated with the possibilities of low-cost monitoring and for the purposes of remote sensing of the Earth’s surface [1, 2]. Most UAVs employ a satellite navigation system (GNSS) and an inertial navigation system (INS) Such navigation solutions, cannot work in the environments with a weak or missing GNSS signal and are not applied under such conditions as navigation in the mountains/hollows, navigation in an enclosed space or a flight in urban conditions. Optical flow (OF) can be used to estimate the speed, orientation, and motion trajectories after the estimated values for OF and INS passed through stochastic filters. Such systems of OF/INS provide tremendous opportunities to support small or micro UAVs in short-range navigation, rather than relying on a GNSS signal. There is a need for new procedures for the motion analysis to navigate in the environments with a weak or missing GNSS signal
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