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首页> 外文会议>2017 International Conference on Communication, Computing and Digital Systems >Stereovision-based real-time obstacle detection scheme for Unmanned Ground Vehicle with steering wheel drive mechanism
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Stereovision-based real-time obstacle detection scheme for Unmanned Ground Vehicle with steering wheel drive mechanism

机译：基于立体视觉的带方向盘驱动的无人地面车辆实时障碍检测方案

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摘要

This paper highlights the importance of an autonomous navigation scheme for an Unmanned Ground Vehicle (UGV) operating under a complex operational scenario that requires obstacle detection. The proposed scheme is based on the construction and investigation of disparity images and follows a two-stage perception structure. During detection phase, the relationship between encountered obstacles and the robot's path is inferred. Based on the extracted Region Of Interest (ROI) and statistical information about projections, the confirmation phase characterizes the contours and obstacles positions. Computationally intensive stereovision techniques are optimized for use in real-time applications. The scheme has been tested on a custom-developed wheeled mobile robot. The experimental findings show the benefits of our scheme. The robot detects obstacles of any size and shape within a range of 80–200cm. The results demonstrate that the robot has the ability to precisely navigate in a wide range of illumination conditions. The proposed approach does not need any extraction of lane markers since it fully exploits the information contained in the disparity images. The anticipated applications of the proposed scheme include autonomous navigation of vehicles for environment exploration, perimeter surveillance and automated delivery of goods in a factory.
机译：本文强调了在需要障碍物检测的复杂操作情况下运行的无人地面车辆（UGV）自主导航方案的重要性。所提出的方案基于视差图像的构造和研究，并且遵循两阶段感知结构。在检测阶段，可以推断遇到的障碍物和机器人路径之间的关系。基于提取的关注区域（ROI）和有关投影的统计信息，确认阶段将表征轮廓和障碍物位置。计算密集型立体视觉技术经过优化，可用于实时应用。该方案已在定制开发的轮式移动机器人上进行了测试。实验结果表明了我们方案的好处。机器人可以检测80-200cm范围内任何大小和形状的障碍物。结果表明，该机器人具有在各种照明条件下精确导航的能力。所提出的方法不需要任何车道标记的提取，因为它充分利用了视差图像中包含的信息。拟议方案的预期应用包括用于环境探索，周界监视和工厂自动化货物运输的车辆自动导航。

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