Robot Object Recognize And Location Based On Image Local Invariant Features

The robot visual perception has been one of the hottest topics. The main goal of the research enables the robot to become more intelligent when perceiving objects around. Yet even a very simple object, it is a very tough task for robot to recognize it. The most key issue is the representation and description of an object, that is, which kind of features can be used to distinguish between one object and another. In recent years the study of local invariant features of the image makes us see the hope to address this issue. For the local invariant features of the image, the core is "invariant". When human identify an object, whether the object is near or far, it can be identified, which is called scale invariance. When the object is rotated, it also can be accurately recognized, which is called rotational invariance. That make the robot have this reception like people is the goal of studying the local invariant features of an image.In this paper, the image of the local invariant features and the binocular cameras of the robot calibration carry out a detailed analysis and description. Firstly, the SIFT and SURF features of the image are extracted, which are invariance to rotation, scale and brightness changes. And then the improved KD-tree algorithm is used to match features between two images. According to perspective transformation between two images, the object position in the scene image is then determined. Thirdly, Zhang's technique is used to calibrate the camera matrix. Then the epipolar geometry and the single camera calibration result are used to calculate the relationship between two cameras. At last, the 3D position of the object is obtained based on the triangulation principle.The experimental result indicates that, SIFT and SURF features are extraordinary robustness against most disturbances such as scaling, rotation and occlusion. The affine transform obtained by RANSAC algorithm maintains reliability when locating the object position in the scene image. The 3D position of the object is also accurate and can be used for robot operation.