Towards a Robust Visual-Inertial-Surround-View SLAM System for Autonomous Indoor Parking

An autonomous parking system is a low-speed unmanned driving system applied in indoor parking environments. Real-time and high-precision vehicle localization and map construction of the environment are two core functional modules of the system. Camera and IMU (Inertial Measurement Unit) sensors provide complementary data to create a Visual-Inertial Simultaneous Localization and Mapping (VI-SLAM) system. However, existing SLAM systems face challenges in complex parking environments. Moreover, limitations inherent in VI-SLAM systems further compromise their perception accuracy, affecting both localization and optimization. This article addresses the shortcomings of current VI-SLAM systems by proposing the RVIS SLAM system. This robust semantic SLAM system integrates data from three sensors: a front-view camera, an IMU, and a surround-view system. To ensure localization accuracy, the system utilizes metric information from common semantic objects on the ground. These objects include parking-slots, speed bumps, and parking-slot numbers captured in surround-view images to build scale-aware constraints. These constraints refine the initial scale of the SLAM system, which is often compromised under low IMU excitation conditions. Additionally, in optimization, SLAM systems ideally assume that the front-end produces optimization graphs without data association outliers. However, in real-world indoor parking environments, sensor noise and vehicle vibrations make this assumption unrealistic. To mitigate the adverse effects of outliers in SLAM systems, this article proposes a robust surround-view semantic data association strategy. This strategy quantifies the uncertainty of surround-view semantic landmarks for the first time, ensuring reliable localization and mapping in challenging environments. Extensive experiments in typical indoor parking environments validate the effectiveness and efficiency of the proposed RVIS SLAM system.