1. 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

2. 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