基于融合特征的无人履带车辆道路类型识别方法

Unmanned tracked vehicles often navigate challenging terrain, and incorporating road types as priori information for tasks such as suspension control, automatic gear decision, and path planning can enhance their performance. However, methods based on single-class features have limitations in accuracy and environmental adaptability. To overcome this, a road-type identification method based on fusion features is proposed, combining deep image features with the statistical features of vertical acceleration in time, frequency, and power spectral density domains. Machine learning classification algorithms are used to identify the road types. Compared to using single class of features, the proposed method using fusion features enriches image features and vertical acceleration features, and improves the accuracy and environmental adaptability. The response speed of the method based on fusion features is similar to that of the image-based methods. Five machine learning classification algorithms are compared. The results show that support vector machine and random forest are the most accurate and fastest classification algorithms, achieving over 90% accuracy with a speed of 14 frames per second.