Binocular vision-based target attitude calculation method

Abstract

The invention provides a binocular vision-based target attitude calculation method, which comprises the steps of (1) collecting multiple groups of images through left and right cameras and completing calibration of the left and right cameras; (2) carrying out correcting and filtering processing on the images collected by the left and right cameras; (3) extracting a target from the images obtained in the step (2), processing to obtain a target contour and a coordinate position of the target in the image; (4) dividing a picture comprising the target from the image in the step (2), extracting feature points in the divided picture and correcting coordinates of the feature points through the position information of the target obtained in the step (3) in the image; (5) selecting the optimal feature point according to the dispersion degree of the feature points; and (6) calculating the target attitude by using the optimal feature point. According to the method, the binocular vision-based feature point coordinate calculation accuracy can be improved, and the feature point matching precision can be improved and the robustness and the stability of an attitude calculation algorithm can be improved.

Description

technical field [0001] The present invention relates to the field of visual navigation, in particular, to a binocular vision-based target attitude calculation method. Background technique [0002] At present, there are many methods for visually solving the target pose, which can be roughly divided into three categories: 1) monocular; 2) binocular; 3) multi-eye. Compared with the binocular system, the target position information obtained by the monocular system is less, while the modeling method of the multi-camera system is more complicated. Methods for extracting target surface features include extracting geometric features, extracting point features, and other methods. However, using the binocular system to obtain the surface features of the target has low requirements on the surface features of the target and obtains more surrounding information, which can improve the calculation accuracy of the target. However, in practical applications, due to the low accuracy of feat...

AI Technical Summary

Problems solved by technology

However, in practical applications, due to the low accuracy of feature point coordinate calculation based on vision and the low accuracy of feature point matching, the accuracy of target attitude calculation is not high.

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