The obstacles detection for outdoor robot based on computer vision in deep learning In this thesis, we propose a guided robot with deep learning techniques and machine vision in order to predict distance and avoid obstacles. The training data images are captured by the stereo camera, and output results are disparity of the single image. Then, the distances are converted by using the triangulation method of computer vision. The back propagation neural network retrains to obtain the actual distance of each pixel in the image. Therefore, the robot with the monocular camera could know the distance between obstacles and itself. Semantic segmentation is utilized to a to distinguish road and obstacles in the image. Fuzzy theory for calculating the area of the road which be cut is designed to avoid walking into the intersection. The obstacle depth and the walkable area are taken as an information for avoiding obstacles control. Because of the robot is proposed to walk on the right side along the road, the edge of road is necessary to navigate the robot. The Hough method is used to find the straight line, and then choose the line we need. The path planning is also achieved in through of navigation and obstacle avoidance control. As a result, the robot can arrive the destination safely and precisely.

Visual Road Boundary Detection based Local Path Planner We propose a realtime visual road detection based local path planner for autonomous navigation in unstructured environments. The system consists of two parts: road detection and local path planning. Road boundary model based on convolution neural network is trained for local road detection in image frame. After perspective transformation, the result is transformed to the vehiclexe2x80x99s frame for path planning. The road midline generated from the road boundaries is used to represent the reference local path. Priori information with the global navigation topology map is combined to solve multiroads selection. Finally, data fusion with historical detection results and predictive control of vehicle model is used for path optimization. In addition, obstacle avoidance combined with LIDAR perception is also taken into account to complete an autonomous navigation task in complex scenarios.A large number of experiments with various road conditions shows that the proposed method has a good adaptability and versatility for unstructured scenarios. The algorithm can effectively reduce the dependence on absolute positioning and LIDAR perception. So it is of great importance for autonomous navigation in scenarios without satellite or in unknown scenarios.