IFAL-SLAM: an approach to inertial-centered multi-sensor fusion, factor graph optimization, and adaptive Lagrangian method

Abstract In response to the issues of low accuracy, perception degradation, and poor reliability of single-sensor simultaneous localization and mapping (SLAM) technologies in complex environments, this study presents a novel Inertial Measurement Unit (IMU)-centered multi-sensor fusion SLAM algorithm (IFAL-SLAM) integrating Light Detection and Ranging (LiDAR), vision, and IMU, based on factor graph elimination optimization ( I MU-centered multi-sensor F usion, A daptive L agrangian methods). The proposed system leverages a multi-factor graph model, centering on the IMU, and applies a covariance matrix to fuse visual-inertial and LiDAR-inertial odometries for bias correction, using loop closure factors for global adjustments. To minimize the optimization costs post-fusion, a sliding window mechanism is incorporated, coupled with a QR decomposition elimination method based on Householder transformation to convert the factor graph into a Bayesian network. Finally, an adaptive Lagrangian relaxation method is proposed, employing matrix-form penalty parameters and adaptive strategies to enhance convergence speed and robustness under high rotational dynamics. Experimental results indicate that the proposed algorithm achieves absolute trajectory errors of approximately 0.58 m and 0.24 m in large and small complex scenes, respectively, surpassing classic algorithms in terms of accuracy and reliability.