Research on Monocular Odometer Based on BEBLID and ORB Algorithm
In order to prevent the feature points extracted by FAST algorithm in the environment of rich texture from being too dense,the preset conditions are used to make the feature points sparse,so as to facili-tate the subsequent polar constraint recovery of camera pose.In order to ensure that there are fewer textures in the image frames taken by the camera and no image information will be lost during feature extraction,the detection method is proposed to adjust according to the number of feature points,so that it can extract im-age features adaptively and enhance its adaptability to complex environment.In order to improve the matc-hing accuracy of image features,the feature point descriptor is fully expressed by incorporating BEBLID.Experimental results show that the improved algorithm has strong environmental robustness in the face of complex scenarios.The matching accuracy of the improved algorithm is higher than that of ORB algorithm,and the time consumption of the algorithm is higher than that of SIFT algorithm.After trajectory calcula-tion,compared with ORB algorithm,the improved algorithm is more in line with the real trajectory in cam-era motion,and has obvious improvement in position pose accuracy.
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