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

一些研究人员已经研究了视觉导航的自主道路车辆使用各种视觉信息源，包括颜色，视差，范围，光流等，这样的研究强调了确定道路的重要性，作为一个重要组成部分的智能道路车辆的发展。在这个项目中，我们提出了三个模型来解决智能车辆的道路跟踪和3D道路形状重建的一般问题。以下三种方法均基于活动轮廓模型（ACM）。1）该方法假设道路边界平行且道路宽度恒定。然后，我们检测和跟踪图像中的道路区域使用耦合的活动轮廓模型的并行约束。然后，系统从单个图像生成3D道路模型。通过应用于包含4000多幅图像的真实的道路场景，我们评估了该方法的有效性 ...更多信息 基于全向传感的导航。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，可以通过主动轮廓模型提取机器人周围的粗糙自由空间。该方法产生低层次的命令，保持机器人在自由空间的中间，并避免碰撞的平衡形状提取的活动轮廓。利用主动轮廓模型，机器人可以通过跟踪闭环曲线来避开障碍物并沿走廊沿着移动。此外，该方法可以在图像坐标系上直接表示环境与机器人之间的空间关系。3）第三种模型用于机器人沿着路径导航。当机器人移动到目标位置时，通过一系列连续的全向图像来记忆路线。当机器人导航到目标点时，输入与记忆的时空路径模式图像匹配，通过使用双活动轮廓模型，我们可以估计准确的机器人位置。少

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