Using Context to Improve Robot Vision
Vision is a rich source of information for robots deployed in the real-world. Although considerable research has been performed in the area of robot vision, existing algorithms are still inadequate for accurate scene understanding and object recognition in the real world. Robots frequently find it difficult to recognize objects and successfully complete the assigned tasks in challenging scenarios with a significant amount of clutter. Improving the ability of robots to fully utilize the information encoded in visual inputs is hence crucial for the widespread deployment of robots. Contextual cues are very important for object recognition in humans. Recent research in computer vision and robot vision has hence focused on using context to improve object recognition on robots. Contextual cues can enable robots to use the known information about some objects in the domain to locate other related objects more effectively. This research project describes the context of objects in images using color histograms and local image gradient (SIFT) features of the neighboring image segments. Once the robot has learned the typical context of desired objects, these objects can be recognized effectively in test images by comparing the context of candidate image regions with the learned context, using the nearest neighbor algorithm. This approach is evaluated on a set of images captured by a camera mounted on a mobile robot.