Design and implementation of family service robots' object recognition based on Webots

摘要

This article based on the current robot to achieve the identification of objects exist a number of issues to design. In order to achieve the robots in the family scene to identify specific objects. Thus verifying its feasibility and practicability. Based on SURF algorithm and SVM classifier to extract local features and training, this paper proposes a PCA algorithm and Bag-of-Visual-Word algorithm to reduce the dimensionality and clustering of extracted features to facilitate SVM training while improving recognition accuracy and reducing computation time. At the same time using multi-view and Image Pyramid segmentation method to solve the occlusion and complex background recognition. All experiments were performed using the Webots robotics development platform and the OpenCV library. Experimental results show that the above method can ensure the real-time performance while ensuring the accuracy of recognition. It has a certain feasibility and practical value.