Efficient and effective vision systems are proposed in this work for object detection for ground&aerial robots venturing into unknown environments with minimum vision aids, i.e. a single camera. The first problem attempted is that of object search and identification in a situation similar to a disaster site. Based on image analysis, typical pixel-based characteristics of a visual marker have been established to search for, using a block based search algorithm, along with a noise and interference filter. The proposed algorithm has been successfully utilized for the International Aerial Robotics competition 2009. The second problem deals with object detection for collision avoidance in 3D environments. It has been shown that a 3D model of the scene can be generated from 2D image information from a single camera flying through a very small arc of lateral flight around the object, without the need of capturing images from all sides. The forward flight simulations show that the depth extracted from forward motion is usable for large part of the image. After analyzing various constraints associated with this and other existing approaches, Motion Estimation has been proposed. Implementation of motion estimation on videos from onboard cameras resulted in various undesirable and noisy vectors. An in depth analysis of such vectors is presented and solutions are proposed and implemented, demonstrating desirable motion estimation for collision avoidance task.
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Single camera based vision systems for ground and; aerial robots
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