碩士 / 國立臺北科技大學 / 機電整合研究所 / 93 / In this thesis, we have developed an implement image process method using watershed transformation and mathematical morphology to detect road by hue, saturation and intensity information. Then we use binocular stereovision system and artificial intelligence (AI) policy to the autonomous land vehicle (ALV) can be navigated at the campus and the pavement. The system is divided into local navigation and global navigation. Before the navigation, the camera calibration is necessary. We employ the linear least-square method to obtain calibration parameters of the left and the right cameras using eight known 3D points and image points projected from real world into cameras. Then we can reconstruct the 3D information from the image points of two cameras by using the calibrated parameters.
Road detection, sub-goal and goal searching are the focuses of the study in navigation. Because the sceneries in the outdoor environment are very complicated, so we need a powerful image segmentation method to find the road candidates. To find the candidates, in our study we use watershed transformation to conform our needs. However, the transformation needs an obvious gradient in the image. Dilation, erosion, opening, and closing are the morphological operations. We use these operations to implement the gradient and reduce the too small information that we do not need in the image. For this reason we can segment several regions of road by watershed transformation. Firstly, since the general road has consistent hue and lower saturation, we employ these features to characterize the candidate region. Meanwhile, we need to use stereovision to obtain 3D information of the environment. So the stereo correspondence is necessary. In the study of the sensor-like points approach [1] is employed to obtain corresponding points and use them to reconstruct the 3D information. In the sub-goal and the goal searching, the string match approach is employed to find out them. In the navigation and path planning, we define some features such as length, angle, area and distance of the objects as a map. Following the map, we find the sub-goal and the goal. In here the sub-goal is a pair of rail lines and the goal is the cross lines on the wall beside the road. When they are found out, it indicates that the ALV has arrived at the position of the sub-goal or the goal and runs toward the goal or stop.
In global navigation, the optimal path planning based on road information and the direction between ALV and the sub-goal or the goal is the study focus. The direction between ALV and the sub-goal or the goal is obtained by an E-compass. We also employ an AI-based navigation method to obtain the angle where ALV has to turn and make ALV avoid the obstacle safely and run toward the sub-goal or the goal in an appropriate path. The ALV system has been performed in the campus and the pavement to demonstrate the effectiveness of the presented method.
| Identifer | oai:union.ndltd.org:TW/093TIT05651006 |
| Date | January 2005 |
| Creators | Guen-Jou Chen, 陳冠州 |
| Contributors | 駱榮欽 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 71 |
Page generated in 0.0075 seconds