Vehicle Motion Estimation Using Visual Observations of the Elevation Surface

We consider the problem of visual odometry for a sequence of video frames using a camera directed perpendicularly downward. We propose an adaptive two-stage visual odometry technology based on sequential determination of interframe shifts and regular correction of current coordinate estimates. At the first stage, the shift between two consecutive frames is determined by the correlation method, with the compared video frames being aligned using the found shift parameters up to a pixel. At the second stage, the shifts are refined with subpixel precision using the optical flow method. To improve reliability, the most consistent estimates of the optical flow are selected. We present the results of experimental studies on publicly available survey data, which confirm the high reliability and accuracy of the estimates.