Markov Random Field based small obstacle discovery over images

Small obstacles of the order of 0.5–3cms and homogeneous scenes often pose a problem for indoor mobile robots. These obstacles cannot be clearly distinguished even with the state of the art depth sensors or laser range finders using existing vision based algorithms. With the advent of sophisticated image processing algorithms like SLIC [1] and LSD [9], it is possible to extract rich information from an image which led us to develop a novel architecture to detect very small obstacles on the floor using a monocular camera. This information is further processed using a Markov Random Field based graph cut formalism that precisely segments the floor and detects obstacles which are extremely low. We show robust and accurate obstacle detection and floor segmentation in diverse environments over a large variety of objects found indoors. In our case, low lying obstacles, changing floor patterns and extremely homogeneous environments are properly classified which leads to a drastic decrease in the number of obstacles that may not be classified by existing robotic vision algorithms.