Parallel VINS-Mono algorithm based on GPUs in embedded devices. (28th January 2022)

• Record Type: Journal Article
• Authors: Lu, Quan; Xu, Jianli; Hu, Likun; Shi, Minghui
• Is Part Of: International journal of advanced robotic systems. Volume 19:Number 1(2022)
• Publication Date: 2022-01-28
• Subjects: VI-SLAM -- parallelization algorithm -- GPU -- CUDA -- factor graph; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/17298814221074534
• Languages: English
• ISSNs: 1729-8806

Abstract

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.