Perspective Reconstruction by Determining Vanishing Points for Autonomous Mobile Robot Visual Localization on Supermarkets

Mobile robots are more and more used on diverse environments to provide useful services. One of these environments are supermarkets, where a robot can help to find and carry products, maintain the account of them and to mark out from a list, the products already in the shopping car (maybe the same robot). However, a common problem on these environments is the autonomous localization, due to the fact that supermarkets are a set of aisles, and most of them look the same for laser range finders; sensors commonly used for localization. On this paper, we present an approach to localize autonomous mobile robots on supermarket by using a perspective reconstruction of the shelves and then an statistical comparison of the products present in them. In order to detect the shelves, the vanishing points are estimated to provide a fast and efficient way to segment products on them. To avoid multiple vanishing points on this kind of environments, result of the variety of products present, a variation of a RANSAC approach is proposed. Once a vanishing point has been determined, an homography process is applied to the shelves in order to rectify images. And finally, by horizontal histograms the robot is able to segment individual products to be compared to the data base. Then the robot will be able to detect by a probability function the correct aisle where it is.