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%.

With the transformation and development of the automotive industry, low-cost and seamless indoor and outdoor positioning has become a research hotspot for modern vehicles equipped with in-vehicle infotainment systems, Internet of Vehicles, or other intelligent systems (such as Telematics Box, Autopilot, etc.). This paper analyzes modern vehicles in different configurations and proposes a low-cost, versatile indoor non-visual semantic mapping and localization solution based on low-cost sensors. Firstly, the sliding window-based semantic landmark detection method is designed to identify non-visual semantic landmarks (e.g., entrance/exit, ramp entrance/exit, road node). Then, we construct an indoor non-visual semantic map that includes the vehicle trajectory waypoints, non-visual semantic landmarks, and Wi-Fi fingerprints of RSS features. Furthermore, to estimate the position of modern vehicles in the constructed semantic maps, we proposed a graph-optimized localization method based on landmark matching that exploits the correlation between non-visual semantic landmarks. Finally, field experiments are conducted in two shopping mall scenes with different underground parking layouts to verify the proposed non-visual semantic mapping and localization method. The results show that the proposed method achieves a high accuracy of 98.1% in non-visual semantic landmark detection and a low localization error of 1.31 m.