Indoor Mobile Robot Localization Applying IMU/Stereo Camera/LiDAR and Graph-Based Optimization

The navigation and positioning system of mobile robots using multisensor fusion has become a research hotspot in the aspects of high accuracy, low computational complexity, and strong stability. In order to improve the accuracy of sensor asynchronous information fusion and meet the application requirements of geometric structure feature degradation in warehousing logistics, an adaptive weighted factor graph (AWFG) positioning method using inertial measurement unit (IMU), LiDAR, and stereo camera is proposed. Combining the dominant features of three sensors and factor graph theory, a new multisensor fusion factor graph model is established. By dynamically adjusting the reliability of sensor measurement information, an adaptive factor weight function is designed to improve positioning accuracy and system stability under abnormal sensor or environmental interference conditions. Besides, sliding window optimization is added to limit the factor scale, and the variable elimination algorithm is combined to optimize the factors in the window to further reduce the computational complexity. Compared with extended Kalman filter (EKF) and particle swarm optimization (PSO) algorithms, simulation and experimental results show that the proposed method not only reduces the mean location error by about 30%, but also effectively enhances the computational efficiency and system stability.