Vanishing point detection in corridors: using hough transform and K-means clustering

One of the main challenges in steering a vehicle or a robot is the detection of appropriate heading. Many solutions have been proposed during the past few decades to overcome the difficulties of intelligent navigation platforms. In this study, the authors try to introduce a new procedure for finding the vanishing point based on the visual information and K-Means clustering. Unlike other solutions the authors do not need to find the intersection of lines to extract the vanishing point. This has reduced the complexity and the processing time of our algorithm to a large extent. The authors have imported the minimum possible information to the Hough space by using only two pixels (the points) of each line (start point and end point) instead of hundreds of pixels that form a line. This has reduced the mathematical complexity of our algorithm while maintaining very efficient functioning. The most important and unique characteristic of our algorithm is the usage of processed data for other important tasks in navigation such as mapping and localisation.