A comparative study of optical flow and traditional sensors in UAV navigation

Abstract

Optical flows have great potential for navigation of small or micro unmanned aerial vehicles (UAVs) in GPS-degraded or GPS-denied environments, inspired by the study of the flight of several insects. This paper focuses on a comparative study between optical flow and traditional navigation sensors with validation provided through UAV flight tests. More specifically, optical flow calculated from videos is compared side-by-side with the corresponding combination of GPS velocity, range, and IMU measurements. Scale invariant feature transform (SIFT) algorithm is used to convert camera videos into optical flows due to its stability and robustness for feature extraction purposes. Four basic motions are analyzed through ground tests including two rotational and two translational motions, with rotation axis parallel/orthogonal to optical axis. The UAV flight data are used for comparisons of more general motions. The flight results show that the measured optical flow has a mean error of 1.10/1.16 pixel per frame and a standard deviation of 1.05/1.18 pixel per frame in the longitudinal/lateral direction for a 33.4 millisecond interval (29.97 Hz), using the corresponding combination of GPS/INS/range data as the ground truth.