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.

传统的视觉-惯性同步定位与建图算法通常是基于CPU设计的，如果直接移植到带有GPU模块的嵌入式板卡上，就无法有效利用GPU的并行计算功能。然而，嵌入式设备的计算能力是有限的。视觉惯性同步定位与建图算法在GPU空闲时占用大部分CPU计算资源是不合理的。本文提出了一种基于GPU并行计算技术的VINS-Mono算法并行化方案。基于计算统一的设备架构，增量方程的构造和求解在算法的非线性优化过程中并行化，并使用cuSOLVER和cuBLAS提供的并行化方法进行算法的边缘化。此外，算法中重写了光流跟踪过程中图像特征点检测与匹配的程序，实现了光流跟踪的并行化。经过并行化后，发现该算法在CPU和GPU组成的异构计算模型上运行良好，可以充分发挥GPU的并行计算能力。该方法在 NVIDIA 的 Jetson TX2 模块上进行了测试，并与 VINS-Mono 算法进行了比较；发现增量方程的构造和求解速度相同，但所提出方案的光流跟踪和边缘化速度分别提高了约 1.5-1.7 倍和 1.9 倍，