Unfalsified Visual Servoing for Simultaneous Object Recognition and Pose Tracking
提出一种数据驱动的非证伪控制方法，通过监督机制验证图像特征与三维模型的匹配，在视觉伺服中同时实现目标识别与跟踪，能自动从匹配和跟踪失败中恢复。
In a complex environment, simultaneous object recognition and tracking has been one of the challenging topics in computer vision and robotics. Current approaches are usually fragile due to spurious feature matching and local convergence for pose determination. Once a failure happens, these approaches lack a mechanism to recover automatically. In this paper, data-driven unfalsified control is proposed for solving this problem in visual servoing. It recognizes a target through matching image features with a 3-D model and then tracks them through dynamic visual servoing. The features can be falsified or unfalsified by a supervisory mechanism according to their tracking performance. Supervisory visual servoing is repeated until a consensus between the model and the selected features is reached, so that model recognition and object tracking are accomplished. Experiments show the effectiveness and robustness of the proposed algorithm to deal with matching and tracking failures caused by various disturbances, such as fast motion, occlusions, and illumination variation.