Abstract
Computer stereo vision is a passive sensing technique that helps to recover 3D information of an environment from 2D images. The stereo correspondence is a challenging task that finds out matching pixels between the stereo image pair based on Lambertian criteria and its result is a disparity space image. The depth of the objects from the camera can be calculated from this disparity value by using the principle of triangulation. For the vision guided robot navigation, the requirement of stereo matching algorithms on low power dedicated hardware that can achieve a high frame rate is unambiguous. A new, highly optimized implementation of correlation based, Sum of Absolute Differences correspondences algorithm on a low cost resource limited FPGA is presented here. This System-on-Programmable-Chip architecture based system achieved a higher frame rate of 50 fps with 64 disparity levels without using a microprocessor. On performance evaluation, the disparity map shows a maximum error of 0.308% only. This reconfigurable parallel processing, high speed architecture of the algorithm implementation takes only 43% of available resources of low density Altera Cyclone II. This hardware implementation of stereo vision system outperforms in terms of accuracy, speed and resource utilization of all the other existing stereo systems of its similar kind. Also, it offers a better trade-off between run-time speed and accuracy and is found suitable for most of the range finding real-time applications.
Highlights
The primary intention of robotic vision is to enable robots to cope with its surroundings in order to perform various tasks like navigation in unknown cluttered environments, moving object tracking and pick and place applications Ude (2010)
The proposed Sum of Absolute Differences algorithm (SAD) stereo matching system has been built on Altera Cyclone II EP2C70 FPGA board using Quartus II software .The algorithm is tested using rectified stereo images provided by Middlebury Stereo Datasets
The performance evaluation has been done on the basis of RMS error and the number of bad matching pixels in the obtained disparity map Scharstein and Szeliski (2003)
Summary
The primary intention of robotic vision is to enable robots to cope with its surroundings in order to perform various tasks like navigation in unknown cluttered environments, moving object tracking and pick and place applications Ude (2010). Based on the Lambertian criteria, the stereo correspondence algorithm detects conjugate pair of pixels between the input stereo images and the result is a disparity space image. Local stereo matching can be of feature based and area based methods that will result in sparse and dense disparity maps respectively. Known as correlation based or window based local approach, a window of required size centered at a pixel of interest from one input image is used to search in the other image for similar intensity levels.
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