Visual perception and navigation of security robot based on deep learning

This work presents a vision based security robot perception and control strategy for semi-structured and unstructured roads navigation. The main contributions contain deep learning technique for road recognition and a hybrid navigation scheme. A deep convolutional neural network is employed to perform pixel-wise segmentation and thus to find road regions. Secondly, based on the segmented regions, an edge extraction algorithm is designed to extract and fit the road boundaries. To ensure the robustness of navigation, the region detection algorithm is proposed to ensure the robot to movement on the traversable area. Experimental results verify the effectiveness of proposed visual navigation approaches.