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、范围、光流等）研究了自动道路车辆的视觉引导导航。此类研究强调了确定前方道路作为开发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车辆的道路跟随和 3D 道路形状重建的一般问题。以下三种方法基于主动轮廓模型（ACM）。1）该方法。假设道路边界是平行的并且宽度然后，我们使用受并行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，然后我们通过以下方式从单个图像生成 3D 道路模型。适用于包含超过 4000 个图像的真实道路场景。2）该方法是基于全向传感的反应式视觉导航。机器人通过全向图像传感器 HyperOmni Vision 投影在输入图像的中心。自由空间该方法可以通过活动轮廓模型提取机器人周围的区域，从而产生低级命令，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，机器人可以避开障碍物并沿着走廊移动。此外，该方法可以直接表示环境与机器人在图像坐标上的空间关系，从而无需进行几何三维重建即可控制机器人。3）第三个模型。用于沿着路线导航机器人。当机器人移动到目标位置时，路线由地平线上的一系列连续全向图像记忆。当机器人导航到目标点时，通过使用双主动轮廓模型将输入与记忆的时空路线模式图像进行匹配。我们可以估计出机器人的准确位置。