Research on the Visual Servoing method based on semantic segmentation results of metal objects in a complex lighting environment

The problem of feature recognition and servo tracking for semantic segmentation results of metal parts under complex and variable lighting conditions is addressed. In this paper, we propose an algorithm that incorporates template matching and image-based visual servoing based on semantic segmentation. The method adopts a geometry-based template matching method to stably identify the image features of the pixel region obtained from semantic segmentation, introduces a feature pyramid dimensionality reduction method to improve the processing speed, and then introduces the extracted image moment features into the image-based visual servoing to operate the robotic arm to perform the servo process on the target.