A Novel Visual-Aided Method to Enhance the Inertial Navigation System of an Intelligent Vehicle in Indoor Environments

Modern inertial navigation system (INS) equipped with high-precision inertial measurement units (IMUs) estimates the pose in real-time with centimeter-level accuracy. However, its drift errors accumulate over a long trajectory because of the bias of IMUs. Therefore, to correct the drift errors and improve the localization accuracy of the system, this paper proposes a novel visual-aided INS for use in intelligent vehicles operating in artificial indoor environments. The vanishing point (VP) extracted from images is used as an external observation without drift errors in the proposed system to estimate the bias of IMUs in an optimization window. Particularly, the sliding window-based local optimization method and the segmentation window-based global optimization method are proposed to improve the system’s localization performance in accuracy under the assumption that the bias of IMUs in an optimization window is a random constant. Considering the effect of scale drifts, the proposed system is scaled using odometry. To evaluate the system’s performance, a series of experiments are conducted in underground parking lots. The experimental results demonstrate that compared to the INS, the average localization errors of the VP-aided INS are improved by 30.77% (0.18m) and 66.99% (0.34m) for Vehicle_01 and Vehicle_02, respectively. And its average attitude errors are improved by 36.36% (0.14°) and 66.26% (0.28°) for Vehicle_01 and Vehicle_02, respectively. Additionally, the proposed optimization methods also apply to the mobile platform equipped with low-cost IMUs, where the localization errors can be reduced by at least 30.46%.