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Visual navigation technology for intelligent vehicle - Road model for autonomous driving and alarming

智能汽车视觉导航技术——自动驾驶与报警道路模型

基本信息

批准号：
12650425
负责人：
YAGI Yasushi
金额：
$ 2.43万
依托单位：
Osaka University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2000
资助国家：
日本
起止时间：
2000 至 2001
项目状态：
已结题

项目摘要

Several researchers have investigated visually-guided navigation of autonomous road vehicles using various sorts of visual information sources, including color, disparity, range, optical flow, etc. Such research has emphasized the importance of determining the road ahead as a significant component of the development of intelligent road vehicles. In this project, we propose three models for solving the general problem of a road following and 3D-road shape reconstruction for a smart vehicle. Three following methods are based on active contour models (ACM).1) The method assumes that road boundaries are parallel and that the width of the road is constant. We then detect and track the road region in the image using coupled active contour models subject to a parallelism constraint. The system then generates a 3D-road model from a single image. We evaluate the effectiveness of the method by applying to real road scenes comprising more than 4000 images.2) The method is for reactive visual navi … More gation based on omnidirectional sensing. The robot is projected at the center of the input image by the omnidirectional image sensor HyperOmni Vision. Therefore, rough free space around the robot can be extracted by active contour model. The method produces low-level commands that keep the robot in the middle of the free space and avoid collision by balancing shape of extracted active contours. The robot can avoid obstacles and move along the corridor by tracking the close-looped curve with an active contour model. Furthermore, the method can directly represent the spatial relations between the environment and the robot on the image coordinate. Thus the method can control the robot without geometrical 3D reconstruction.3) Third model is for navigating the robot along a route. The route is memorized by a series of consecutive omnidirectional images at the horizon while the robot moves to the goal position. While the robot is navigating to the goal point, the input is matched with memorized spatio-temporal route pattern images by using dual active contour models and we can estimate the exact robot position. Less

一些研究人员使用各种视觉信息源，包括颜色、视差、距离、光流等，研究了自主道路车辆的视觉导航。这些研究强调了确定前方道路作为智能道路车辆发展的重要组成部分的重要性。在这个项目中，我们提出了三种模型来解决智能汽车的道路跟踪和三维道路形状重建的一般问题。以下三种方法是基于活动轮廓模型(ACM)的。1)该方法假定道路边界是平行的，道路宽度是恒定的。然后，我们使用受并行度约束的耦合活动轮廓模型来检测和跟踪图像中的道路区域。然后，该系统从一张图像中生成3D道路模型。通过对包含4,000多幅图像的真实道路场景的应用，对该方法的有效性进行了评估。2)该方法适用于反应式视觉导航…更多基于全方位传感的导航。机器人通过全方位图像传感器HyperOmni Vision投影在输入图像的中心。因此，可以通过活动轮廓模型提取机器人周围的粗略自由空间。该方法产生低级命令，使机器人保持在自由空间的中间，并通过平衡提取的活动轮廓的形状来避免碰撞。通过主动轮廓模型跟踪闭合曲线，机器人可以避开障碍物并沿走廊移动。此外，该方法还可以在图像坐标上直观地表示环境与机器人之间的空间关系。因此，该方法无需进行几何3D重建即可对机器人进行控制。3)第三个模型用于导航机器人沿一条路径。当机器人移动到目标位置时，通过地平线上一系列连续的全方位图像来记忆路径。当机器人导航到目标点时，利用双活动轮廓模型将输入与记忆的时空路径图像进行匹配，从而估计出机器人的准确位置。较少