Vector Disparity Sensor with Vergence Control for Active Vision Systems
Vector Disparity Sensor with Vergence Control for Active Vision Systems
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DOI：
10.3390/s120201771
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发表时间：
2012-02-01
期刊：
影响因子：
3.9
通讯作者：
Ros, Eduardo
中科院分区：
文献类型：
--
作者：
Barranco, Francisco;Diaz, Javier;Ros, Eduardo
本文提出了一种用于机器人应用的主动视觉系统计算矢量视差的架构。双目系统的聚散角的控制使我们能够有效地探索动态环境，但需要相对于静态相机设置的视差计算的泛化，其中视差在图像校正后严格为1-D。视觉和运动控制之间的相互作用使我们能够开发一种主动传感器，该传感器实现了注视点周围的视差计算的高精度，以及用于聚散度控制的快速反应时间。在这方面的贡献，我们解决了使用FPGA设备的矢量视差计算的实时架构的发展。我们实现了视差单元和控制模块的聚散度，版本和倾斜，以确定固定点。此外，基于双目图像的亮度（基于梯度）和相位信息，讨论了两种片上不同的矢量视差引擎的替代方案。这些引擎的多尺度版本能够在VGA分辨率图像上以非常好的精度估计高达32 fps的矢量视差，如使用具有已知地面实况的基准序列所示。在帧速率，资源利用率，和所提出的方法的准确性方面的性能进行了讨论。在这些结果的基础上，我们的研究表明，基于梯度的方法导致最佳的权衡选择与主动视觉系统的集成。
This paper presents an architecture for computing vector disparity for active vision systems as used on robotics applications. The control of the vergence angle of a binocular system allows us to efficiently explore dynamic environments, but requires a generalization of the disparity computation with respect to a static camera setup, where the disparity is strictly 1-D after the image rectification. The interaction between vision and motor control allows us to develop an active sensor that achieves high accuracy of the disparity computation around the fixation point, and fast reaction time for the vergence control. In this contribution, we address the development of a real-time architecture for vector disparity computation using an FPGA device. We implement the disparity unit and the control module for vergence, version, and tilt to determine the fixation point. In addition, two on-chip different alternatives for the vector disparity engines are discussed based on the luminance (gradient-based) and phase information of the binocular images. The multiscale versions of these engines are able to estimate the vector disparity up to 32 fps on VGA resolution images with very good accuracy as shown using benchmark sequences with known ground-truth. The performances in terms of frame-rate, resource utilization, and accuracy of the presented approaches are discussed. On the basis of these results, our study indicates that the gradient-based approach leads to the best trade-off choice for the integration with the active vision system.