Optical Flow For Vision-Aided Navigation Goal: Use vision to detect displacement between images to aid navigation On unmanned aerial vehicles (UAVs), usually have low-quality cameras and low frame rates giving large displacements between images Also, limited computing power Feature matching works pretty well, but is computationally expensive Motion is too large for standard optical flow techniques, but algorithms computing optical flow at different scales can be effective Sample aerial images with large displacement SIFT features matched between those images Elizabeth Boroson, Stanford University 1

Optical Flow For Vision-Aided Navigation Implemented feature-matching and optical flow algorithms to compare performance on images with large displacements For small displacements, algorithms performed equally well. Optical flow had more outliers, but feature-matching had more missing points. For large displacements, optical flow results did not match feature-matching “truth” Possible causes: changes in image intensity, motion too large for largest scale, outliers skewing results Future work: modify optical flow algorithm to work at larger scales and reduce sensitivity to overall image brightness Response of both algorithms to 5-pixel horizontal displacement of a small image. Elizabeth Boroson, Stanford University 2