A Novel Real-Time Autonomous Localization Algorithm Based on Weighted Loosely Coupled Visual–Inertial Data of the Velocity Layer

Abstract

IMUs (inertial measurement units) and cameras are widely utilized and combined to autonomously measure the motion states of mobile robots. This paper presents a loosely coupled algorithm for autonomous localization, the ICEKF (IMU-aided camera extended Kalman filter), for the weighted data fusion of the IMU and visual measurement. The algorithm fuses motion information on the velocity layer, thereby mitigating the excessive accumulation of IMU errors caused by direct subtraction on the positional layer after quadratic integration. Furthermore, by incorporating a weighting mechanism, the algorithm allows for a flexible adjustment of the emphasis placed on IMU data versus visual information, which augments the robustness and adaptability of autonomous motion estimation for robots. The simulation and dataset experiments demonstrate that the ICEKF can provide reliable estimates for robot motion trajectories.