A Visual Odometry Algorithm Based on Feature Point Method and Optical Flow Tracking
Aiming at the problem that the feature point method in visual odometry has a large amount of calculation and leads to poor real-time effect,a fusion algorithm of feature point method and optical flow tracking is proposed.Firstly,the keyframes are selected by the parallax threshold that measures the move-ment and by the tracking attenuation coefficient of the map points as the screening strategy.Then,the de-scriptors are only calculated for the feature points in the key frames.And for the feature points in the non-key frames,the correspondence between the key points is established by the sparse optical flow method based on the image pyramid.In this condition,the amount of calculation is reduced.Finally,the pose esti-mation of the current frame is obtained by minimizing the reprojection error.It is showed that the positio-ning accuracy of the proposed algorithm is comparable to the ORBSLAM2 algorithm,and the running speed can be increased by more than 70%,meanwhile the real-time performance is obviously better than the ORBSLAM2 algorithm in the experimental test.
visual odometryfeature point methodoptical flow trackingkey framepose estimation
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