Integrating disparity and edge detection algorithms to autonomously follow linear-shaped structures at low altitude

One of the main requirements in enabling autonomous flight of Micro Aerial Vehicles is the ability of autonomous navigation. One possible solution to solve this navigation problem is to use vision-based line-following algorithms. Such vision-based algorithm could rely on the various linear structures, which are present in the human constructed environment. Edge detection and disparity estimation have proven to be strong algorithms for the detection of nearby objects. However, these detection algorithms have their weaknesses. The candidate lines found by both algorithms are input for the Probabilistic Hough Transform, which is used to select the best candidate and to determine a directional vector from this line. This paper is a survey for the experimental circumstances to fairly test both algorithms for a line-following task, which is one of the challenges of the Indoor Micro Aerial Vehicle competition.