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首页> 外文会议>Unmanned Systems Technology VIII pt.2 >Using Advanced Computer Vision Algorithms on Small Mobile Robots
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Using Advanced Computer Vision Algorithms on Small Mobile Robots

机译：在小型移动机器人上使用高级计算机视觉算法

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摘要

The Technology Transfer project employs a spiral development process to enhance the functionality and autonomy of mobile robot systems in the Joint Robotics Program (JRP) Robotic Systems Pool by converging existing component technologies onto a transition platform for optimization. An example of this approach is the implementation of advanced computer vision algorithms on small mobile robots. We demonstrate the implementation and testing of the following two algorithms useful on mobile robots: 1) object classification using a boosted Cascade of classifiers trained with the Adaboost training algorithm, and 2) human presence detection from a moving platform. Object classification is performed with an Adaboost training system developed at the University of California, San Diego (UCSD) Computer Vision Lab. This classification algorithm has been used to successfully detect the license plates of automobiles in motion in real-time. While working towards a solution to increase the robustness of this system to perform generic object recognition, this paper demonstrates an extension to this application by detecting soda cans in a cluttered indoor environment. The human presence detection from a moving platform system uses a data fusion algorithm which combines results from a scanning laser and a thermal imager. The system is able to detect the presence of humans while both the humans and the robot are moving simultaneously. In both systems, the two aforementioned algorithms were implemented on embedded hardware and optimized for use in real-time. Test results are shown for a variety of environments.
机译：技术转让项目采用螺旋式开发流程，通过将现有组件技术整合到过渡平台上进行优化，从而增强了联合机器人计划（JRP）机器人系统库中移动机器人系统的功能和自主性。这种方法的一个示例是在小型移动机器人上实现高级计算机视觉算法。我们演示了以下两种对移动机器人有用的算法的实现和测试：1）使用经过Adaboost训练算法训练的增强级联分类器对对象进行分类，以及2）从移动平台进行人的存在检测。对象分类是使用由加利福尼亚大学圣地亚哥分校（UCSD）计算机视觉实验室开发的Adaboost训练系统执行的。该分类算法已用于成功地实时检测行驶中的汽车的车牌。在努力寻求一种提高该系统执行通用对象识别的鲁棒性的解决方案的同时，本文通过在杂乱的室内环境中检测汽水罐展示了该应用程序的扩展。来自移动平台系统的人类存在检测使用数据融合算法，该算法将扫描激光和热成像仪的结果进行组合。该系统能够在人和机器人同时移动时检测到人的存在。在这两个系统中，上述两种算法都是在嵌入式硬件上实现的，并针对实时性进行了优化。显示了针对各种环境的测试结果。

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