Traditional visual-inertial simultaneous localization and mapping algorithms are usually designed based on CPUs, and they cannot effectively utilize the parallel computing function of GPUs if they are directly transpl...
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Traditional visual-inertial simultaneous localization and mapping algorithms are usually designed based on CPUs, and they cannot effectively utilize the parallel computing function of GPUs if they are directly transplanted to an embedded board with a GPU module. However, the computing power of embedded devices is limited. It is unreasonable for the visual-inertial simultaneous localization and mapping algorithm to occupy most CPU computing resources while the GPU is idle. In this article, a parallelization scheme for the VINS-Mono algorithm based on GPU parallel computing technology is proposed. Based on the compute unified device architecture, the construction and solution of the incremental equation are parallelized in the nonlinear optimization process of the algorithm, and the parallelization methods provided by cuSOLVER and cuBLAS are used to carry out the marginalization of the algorithm. In addition, the program for the detection and matching of image feature points in the process of optical flow tracking is rewritten in the algorithm to realize the parallelization of optical flow tracking. After parallelization, the algorithm is found to run well on a heterogeneous computing model composed of a CPU and GPU and can fully exploit the parallel computing power of the GPU. The proposed method was tested on an NVIDIA's Jetson TX2 module and compared with the VINS-Mono algorithm;the speeds of the construction and solution of the incremental equation were found to be the same, but the optical flow tracking and marginalization speed of the proposed scheme exhibited improvements of about 1.5-1.7 times and 1.9 times, respectively.
Tomography reconstruction is the process of quickly reconstructing the original image form the projection obtained by X-ray radiation. At present, the high-resolution detector of the Shanghai Synchrotron Radiation Fac...
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ISBN:
(数字)9781728165509
ISBN:
(纸本)9781728165516
Tomography reconstruction is the process of quickly reconstructing the original image form the projection obtained by X-ray radiation. At present, the high-resolution detector of the Shanghai Synchrotron Radiation Facility (SSRF) can scan more than 4GB of tomographic data every 1.5 seconds, and the transmission speed is increased to more than 100GB s -1 . With the upgrade of high-resolution detectors and the increase of data transmission volume, the reconstruction computation on cloud has become a bottleneck in improving the speed of tomography reconstruction even if the fastest Gridrec algorithm is adopted. In this paper, we propose an improved serial Gridrec algorithm and a parallel Gridrec algorithm by improving the convolution kernel to optimize the speed of existing image reconstruction algorithms on low cost GPUs for edge computing. On these GPUs, the multi-threaded tomography reconstruction algorithm not only guarantees high-quality results, but also improves the reconstruction speed over original Gridrec algorithm by more than 11x, and over the classic FBP algorithm by more than 234x. Besides the significant speedup, our work would be the first parallel implementation of Gridrec algorithm on GPU for edge computing.
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