Vision Based Obstacle Avoidance Techniques

Vision is one of the most powerful and popular sensing method used for autonomous navigation. Compared with other on-board sensing techniques, vision based approaches to navigation continue to demand a lot of attention from the mobile robot research community. This is largely due to its ability to provide detailed information about the environment, which may not be available using combinations of other types of sensors. One of the key research problems in mobile robot navigation is the focus on obstacle avoidance methods. In order to cope this problem, most autonomous navigation systems rely on range data for obstacle detection. Ultrasonic sensors, laser rangefinders and stereo vision techniques are widely used for estimating the range data. However all of these have drawbacks. Ultrasonic sensors suffer from poor angular resolution. Laser range finders and stereo vision systems are quite expensive, and computational complexity of the stereo vision systems is another key challenge (Saitoh et al., 2009). In addition to their individual shortcomings, Range sensors are also unable to distinguish between different types of ground surfaces, such as they are not capable of differentiating between the sidewalk pavement and adjacent flat grassy areas. The computational complexity of the avoidance algorithms and the cost of the sensors are the most critical aspects for real time applications. Monocular vision based systems avoid these problems and are able to provide appropriate solution to the obstacle avoidance problem. There are two fundamental groups of vision based obstacle avoidance techniques; those that compute the apparent motion, and those that rely on the appearance of individual pixels for monocular vision based obstacle avoidance systems. First group is called as Optical flow based techniques, and the main idea behind this technique is to control the robot using optical flow, from which heading of the observer and time-to-contact values are obtained (Guzel & Bicker, 2010). One way of the control using these values is by acting to achieve a certain type of flow. For instance, to maintain ambient orientation, the type of Optic flow required is no flow at all. If some flow is detected, then the robot should change the forces produced by its effectors so as to minimize this flow, based on Law of Control (Contreras, 2007). A second group is called Appearance Based methods rely on basic image processing techniques, and consist of detecting pixels different in appearance than that of the ground and classifying them as obstacles. The algorithm performs in real-time, provides a highresolution obstacle image, and operates in a variety of environments (DeSouza & Kak, 2002). The main advantages of these two conventional methods are their ease of implementation and high availability for real time applications. However optical flow based methods suffer from two major problems, which are the illumination problem that varies with time and the