Vector Disparity Sensor with Vergence Control for Active Vision Systems

Abstract

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.

A Survey on FPGA-Based Sensor Systems: Towards Intelligent and Reconfigurable Low-Power Sensors for Computer Vision, Control and Signal Processing

“…Global approaches result in more accurate results but at a higher computational cost. In [ 100 ] Barranco et al implement two different alternatives to compute the vector disparity for an active vision system: a gradient-based technique, the local algorithm of Lucas and Kanade and a phase-based one detailed in [ 101 ] (also a local algorithm). The first technique estimates small local disparities assuming the intensity or brightness constancy of a pixel between left and right images, while the second one computes the disparity using the phase information for different orientations, in a contrast-independent way.…”

Definition of Linear Color Models in the RGB Vector Color Space to Detect Red Peaches in Orchard Images Taken under Natural Illumination

“…The estimate of the diameter can be expressed directly in millimeters if an estimate of the distance between the camera and the fruit is available by using a complementary LIDAR sensor [ 38 – 40 ], by using a second camera to create a stereo image to estimate the image depth [ 41 , 42 ], or by taking advantage of the continuous displacement of the camera to combine images with different perspectives of the fruit [ 43 , 44 ].…”