Outdoor terrain recognition based on transfer learning

Terrain recognition exerts an extremely important role in outdoor mobile robot gait planning, speed control, environment perception, etc. Compared with the traditional terrain recognition process that uses color, texture, and other underlying features to describe terrain images, the present study starts from the perspective of transfer learning. MobileNet and DenseNet are employed for high-level feature extraction, and the voting integrated learning algorithm is used to classify high-level feature data sets. In the meanwhile, we have established an outdoor terrain data set that conforms to the traveling process of outdoor mobile robots, and processed the collected video data with key frames and sliding windows. The accuracy of the classification results reached 97%, basically satisfying the needs of actual terrain recognition.