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首页> 外文会议>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：基于立体型地面车辆与方向盘驱动机构的实时障碍物检测方案

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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：本文突出了自主导航方案在需要障碍物检测的复杂操作场景下运行的无人面地面车辆（UGV）的重要性。该拟议方案基于差异图像的构建和调查，并遵循两级感知结构。在检测阶段期间，推断出遇到的障碍物与机器人路径之间的关系。基于提取的感兴趣区域（ROI）和关于投影的统计信息，确认阶段表征了轮廓和障碍位置。计算密集型立体技术经过优化用于实时应用。该方案已经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计划的好处。机器人检测到80-200cm范围内任何尺寸和形状的障碍物。结果表明，机器人能够精确地在各种照明条件下导航。该方法不需要任何提取车道标记，因为它充分利用了差异图像中包含的信息。拟议计划的预期申请包括用于环境勘探，外围监测和自动交付工厂的自动导航。

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