Vision-based Autonomous Land Vehicle Guidance in Outdoor Road Environments Using Feature Clustering and Shape Matching Techniques
|關鍵字:||自動車;電腦視覺;循線及循路;急轉彎路;汽車;障礙物偵測及避碰;上下坡路;彩色分群;Autonomous Land Vehicle;Computer Vision;Line and Road Following;Sharp-curved Road;Cars;Obstacle Detection and Avoidance;Ascending and Descending Roads;Color information Clustering|
Autonomous land vehicles (ALV's) are useful for many automation applications in both indoor and outdoor environments. Successful ALV navigation requires integration of techniques of environment sensing and learning, image processing and feature extraction, ALV location, path planning, wheel control, and so on. In outdoor environments, because of the great variety of object and road conditions like irregular and unstable features on objects, moving objects, shadows, degraded regions, curved roads, ascending or descending roads, changes of illumination, and even rain, we need to combine different problem-solving algorithms and perhaps equip multiple sensors to solve the complex problem of ALV guidance in roads. In this dissertation, five approaches to vision-based ALV guidance in outdoor road environments are proposed. The conventional ways of ALV guidance, which are generally complex and time-consuming, are avoided in the proposed approaches; instead, efficient and effective ways of ALV guidance, which are usually easier and faster, are adopted. In the first approach, color information clustering and combined line and road following techniques are used for ALV guidance on straight roads with constant widths. The clustering algorithm is used to solve the problem caused by great changes of intensity in navigation. The combined line and road following technique is used to achieve faster and more flexible navigations. To locate the ALV for line following or road following, the line-model or road-model, which are constructed using path lines or road boundaries, are matched with the extracted path lines or road surface in the image, respectively. In the second approach, three tangent lines, that are extracted from the dotted central path line and collected from the images of the previous and current cycles, are used to judge whether the ALV is leaving a straight road and entering a curved road in the current cycle. When the ALV enters a curved road, the three tangent lines collected so far are used again to derive the navigation path at a curved turning road section. The navigation path is assumed to be a circle and is re-derived cyclically for safe navigation. Moreover, the three tangent lines can also be used to judge whether the ALV is leaving a curved road and entering a straight road. The third approach allows variations of road widths, which are caused by existence of static cars on the roadside or moving cars on the road lane. The conventional way of detecting obstacles and cars in the navigation route, which is in general complex and inefficient, is avoided; instead, collision-free road area detecting, which is usually easier and faster, is adopted. Road boundaries are used to construct the reference model, and the road surface intensity is selected as the visual feature in this approach. The reference model is then matched with the extracted road surface in the image to find the safe road area and the ALV location on the safe road area. In the fourth approach, image sequence and coordination transformation techniques are used to detect obstacles ahead on the safe road area in navigation. To judge whether one object newly appearing in the image of the current cycle is an obstacle, the object boundary shape is first extracted from the image. After the translation vector from the ALV location in the current cycle to that in the next cycle is estimated, the position of the boundary shape in the image of the next cycle is predicted using coordinate transformation techniques. The predicted boundary shape is then matched with the extracted boundary shape of the object in the image of the next cycle to judge whether the object is an obstacle. In the fifth approach, the ALV can keep driving forward even when ascending or descending roads appear ahead of the ALV. When the ALV keeps driving on a flat road, it detects and follows the flat road. When the ALV navigates at a transition from a flat road into an ascending road, both of the flat and the ascending road boundaries are extracted from the image, which are then used to estimate the slant angle of the ascending road and compute accordingly the connection points of the flat and the ascending road boundaries. When the ALV drives on the flat road but the flat road boundaries disappear from the image, the flat road boundaries are derived using the estimated slant angle, the stable features of the ascending road boundaries in the image, and the predicted connection points of the ascending and the flat road boundaries. The ALV then follows the derived flat road because it still drives on the flat road. At the beginning of subsequent each navigation cycle, the ALV predicts the flat road boundaries in the image using the derived flat road boundaries just described in the previous cycle. The predicted flat road boundaries are then matched with the extracted ascending road boundaries in the image to judge whether the ALV has entered the ascending road in the current cycle. This way of guidance is also used when the ALV navigates at a transition from a flat road into a descending road. A lot of successful navigation tests show that the proposed approaches are effective for ALV guidance in outdoor road environments.
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