多机器人跟踪中基于特征的运动目标协方差匹配

H. Min, N. Papanikolopoulos, Christopher E. Smith, V. Morellas
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引用次数: 6

摘要

在这项工作中,我们提出了一种运动目标分割技术,并将其应用于基于视觉的机器人跟踪问题。自主多机器人跟随的能力对于许多机器人团队应用非常有用;然而,当机器人只能携带一个小相机或当它们表现出不可预测的运动时,问题就变得非常具有挑战性。当摄像机处于运动状态时,分割运动目标的能力对于解决这一问题至关重要,也是我们工作的重点。我们的贡献包括:(i)使用基于特征的协方差矩阵匹配目标;(ii)利用基于傅里叶变换的特征增强匹配性能;(iii)为没有已知目标模型的情况初始化目标模型。将该方法与尺度不变特征变换和现有的协方差匹配方法进行了比较。然后通过实际机器人实验验证了我们提出的分割方法。
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Feature-based covariance matching for a moving target in multi-robot following
In this work we present a moving target segmentation technique and apply it to a vision-based robot-following problem. The capability to do autonomous multi-robot following is useful for many robot-team applications; however, the problem becomes very challenging when the robots can carry only a small camera or when they exhibit unpredictable motion. The ability to segment a moving target while the camera is also in motion is critical to the solution of this problem and is the focus of our work. Our contributions include: (i) Matching targets using feature-based covariance matrices; (ii) Enhancing matching performance by using features based upon the Fourier transform; and (iii) Initializing a target model for cases without a known target model. We compare the proposed method with the scale-invariant feature transform and existing covariance matching methods. We then validate our proposed segmentation method through real-robot experiments.
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