A visual terrain classification method for mobile robots' navigation based on convolutional neural network and support vector machine. (February 2022)

• Record Type: Journal Article
• Authors: Wang, Wanli; Zhang, Botao; Wu, Kaiqi; Chepinskiy, Sergey A; Zhilenkov, Anton A; Chernyi, Sergei; Krasnov, Aleksandr Yu
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 44:Number 4(2022)
• Page Start: 744
• Page End: 753
• Publication Date: 2022-02
• Subjects: Mobile robots -- deep learning -- support vector machine -- visual terrain classification; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/0142331220987917
• Languages: English
• ISSNs: 0142-3312

Abstract

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.