Unstructured road recognition using self-supervised multilayer perceptron online learning algorithm

A self-supervised multilayer perceptron online learning algorithm was proposed to improve the adaptability and real-time performance for unmanned ground vehicle unstructured road recognition. The road recognition results were used to update the training data set characteristic vector, and an evaluation function was created to trigger classifier retraining, as a result, the current classifier can recognize the road surface efficiently. Also, in the algorithm the processing operations such as the road surface image data sampling, classifier training, training data set updating and classifier recognition were calculated in their own threading. The structure can take advantage of faster classification calculation character of multilayer perceptron, and overcome its problem of time consuming training process. The real vehicle road recognition tests show that the proposed algorithm has a better adaptability and can meet the real-time requirements of unmanned ground vehicle unstructured road navigation.