An improved feature point-based LK optical flow algorithm for robotic arm control

Abstract

Robot vision technology has been widely used in many fields since its emergence, and real-time control of robotic arm control systems has long been a challenging problem. When using optical flow algorithms for tracking in conventional vision control, its accuracy and large movements are affected by the size of the integration window to track fast-moving objects, making it difficult to achieve real-time control in robotic arm vision control. By using a smaller integration window, the Pyramid Lucas-Kanade (LK) method can solve the problem that large motions cannot be tracked, but the basic Pyramid LK method is not very accurate. Therefore, an improved LK optical flow method is proposed for the practical application of the traditional LK method for robotic arm control with poor real-time performance and accuracy, applying a combination of the improved FAST (Features from Accelerated Segment Test) corner point detection and the pyramid LK optical flow algorithm. With the improved FAST algorithm, the corner points with the strongest grey-scale variations can be extracted quickly. This approach allows better estimation of optical flows with strong corner points to track moving objects. The corner points calculated by applying the improved FAST feature point detection are first used as candidate feature points; then the candidate feature points are re-extracted by setting filtering conditions with the information obtained from the robotic arm; and finally, the target feature points are tracked using the LK optical flow method.