Acceleration of stereo-matching on multi-core CPU and GPU

This paper presents an accelerated version of a dense stereo-correspondence algorithm for two different parallelism enabled architectures, multi-core CPU and GPU. The algorithm is part of the vision system developed for a binocular robot-head in the context of the CloPeMa 1 research project. This research project focuses on the conception of a new clothes folding robot with real-time and high resolution requirements for the vision system. The performance analysis shows that the parallelised stereo-matching algorithm has been significantly accelerated, maintaining 12x and 176x speed-up respectively for multi-core CPU and GPU, compared with non-SIMD singlethread CPU. To analyse the origin of the speed-up and gain deeper understanding about the choice of the optimal hardware, the algorithm was broken into key sub-tasks and the performance was tested for four different hardware architectures

Acceleration of stereo-matching on multi-core CPU and GPU

This paper presents an accelerated version of a dense stereo-correspondence algorithm for two different parallelism enabled architectures, multi-core CPU and GPU. The algorithm is part of the vision system developed for a binocular robot-head in the context of the CloPeMa 1 research project. This research project focuses on the conception of a new clothes folding robot with real-time and high resolution requirements for the vision system. The performance analysis shows that the parallelised stereo-matching algorithm has been significantly accelerated, maintaining 12x and 176x speed-up respectively for multi-core CPU and GPU, compared with non-SIMD singlethread CPU. To analyse the origin of the speed-up and gain deeper understanding about the choice of the optimal hardware, the algorithm was broken into key sub-tasks and the performance was tested for four different hardware architectures

Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices

For many applications in low-power real-time robotics, stereo cameras are the sensors of choice for depth perception as they are typically cheaper and more versatile than their active counterparts. Their biggest drawback, however, is that they do not directly sense depth maps; instead, these must be estimated through data-intensive processes. Therefore, appropriate algorithm selection plays an important role in achieving the desired performance characteristics. Motivated by applications in space and mobile robotics, we implement and evaluate a FPGA-accelerated adaptation of the ELAS algorithm. Despite offering one of the best trade-offs between efficiency and accuracy, ELAS has only been shown to run at 1.5-3 fps on a high-end CPU. Our system preserves all intriguing properties of the original algorithm, such as the slanted plane priors, but can achieve a frame rate of 47fps whilst consuming under 4W of power. Unlike previous FPGA based designs, we take advantage of both components on the CPU/FPGA System-on-Chip to showcase the strategy necessary to accelerate more complex and computationally diverse algorithms for such low power, real-time systems.Comment: 8 pages, 7 figures, 2 table

Efficient and accurate stereo matching for cloth manipulation

Due to the recent development of robotic techniques, researching robots that can assist in everyday household tasks, especially robotic cloth manipulation has become popular in recent years. Stereo matching forms a crucial part of the robotic vision and aims to derive depth information from image pairs captured by the stereo cameras. Although stereo robotic vision is widely adopted for cloth manipulation robots in the research community, this remains a challenging research task. Robotic vision requires very accurate depth output in a relatively short timespan in order to successfully perform cloth manipulation in real-time. In this thesis, we mainly aim to develop a robotic stereo matching based vision system that is both efficient and effective for the task of robotic cloth manipulation. Effectiveness refers to the accuracy of the depth map generated from the stereo matching algorithms for the robot to grasp the required details to achieve the given task on cloth materials while efficiency emphasizes the required time for the stereo matching to process the images. With respect to efficiency, firstly, by exploring a variety of different hardware architectures such as multi-core CPU and graphic processors (GPU) to accelerate stereo matching, we demonstrate that the parallelised stereo-matching algorithm can be significantly accelerated, achieving 12X and 176X speed-ups respectively for multi-core CPU and GPU, compared with SISD (Single Instruction, Single Data) single-thread CPU. In terms of effectiveness, due to the fact that there are no cloth based testbeds with depth map ground-truths for evaluating the accuracy of stereo matching performance in this context, we created five different testbeds to facilitate evaluation of stereo matching in the context of cloth manipulation. In addition, we adapted a guided filtering algorithm into a pyramidical stereo matching framework that works directly for unrectified images, and evaluate its accuracy utilizing the created cloth testbeds. We demonstrate that our proposed approach is not only efficient, but also accurate and suits well to the characteristics of the task of cloth manipulations. This also shows that rather than relying on image rectification, directly applying stereo matching to unrectified images is effective and efficient. Finally, we further explore whether we can improve efficiency while maintaining reasonable accuracy for robotic cloth manipulations (i.e.~trading off accuracy for efficiency). We use a foveated matching algorithm, inspired by biological vision systems, and found that it is effective in trading off accuracy for efficiency, achieving almost the same level of accuracy for both cloth grasping and flattening tasks with two to three fold acceleration. We also demonstrate that with the robot we can use machine learning techniques to predict the optimal foveation level in order to accomplish the robotic cloth manipulation tasks successfully and much more efficiently. To summarize, in this thesis, we extensively study stereo matching, contributing to the long-term goal of developing effective ways for efficient whilst accurate robotic stereo matching for cloth manipulation

Implementation of Stereo Matching Using High Level Compiler for Parallel Computing Acceleration

International audienceHeterogeneous computing system increases the performance of parallel computing in many domain of general purpose computing with CPU, GPU and other accelerators. With Hardware developments, the software developments like Compute Unified Device Architecture(CUDA) and Open Computing Language (OpenCL) try to offer a simple and visualized tool for parallel computing. But it turn out to be more difficult than programming on CPU platform for optimization of performance. For one kind of parallel computing application, there are different configuration and parameters for various hardware platforms. In this paper, we apply the Hybrid Multi-cores Parallel Programming(HMPP) to automatic-generates tunable code for GPU platform and show the result of implementation of Stereo Matching with detailed comparison with C code version and manual CUDA version. The experimental results show that the default and optimized HMPP have the approximative 1 compared with CUDA implementation. And the HMPP workbench can greatly reduce the time of application development using parallel computing device