EV-VINS: An Efficient and Versatile Binocular Visual-Inertial SLAM Algorithm

Abstract

To address the problem that the current optimization-based visual-inertial SLAM algorithms have difficulty in achieving high accuracy, high robustness and high efficiency at the same time, an efficient and versatile SLAM algorithm (EV-VINS) based on the improvement of VINS-Mono is proposed in this paper. EV-VINS tightly coupling the binocular and inertial measurement unit is divided into decoupled front-end, which detects GFTT corners and performs optical flow tracking, and back-end, which mainly performs initialization, nonlinear optimization and outlier rejection. The dense map module is added for subsequent navigation functions. In particular, a feature classification strategy is proposed based on the analysis of the existing problems in the initialization, which not only greatly reduces the number of outliers during initialization, but also makes the points used for back-end optimization have good initial values. Therefore, the computational efficiency and the stability of the back-end optimization are significantly improved while ensuring the accuracy. The simulation experiments based on the EuRoC datasets and actual test results show that EV-VINS has better real-time performance while having the basically same location accuracy compared to the classical algorithms.