Indoor mobile robot localization system based on ORB-SLAM3 and multi-sensor fusion

Indoor localization is a fundamental capability for autonomous mobile robots operating in complex indoor environments, where visual degradation, sensor noise, and rotational motion often lead to accumulated drift. This paper presents MFL-SLAM, a practical multi-sensor fusion localization system that extends the ORB-SLAM3 framework by explicitly integrating wheel odometry with visual–inertial SLAM. Unlike conventional visual–inertial approaches, MFL-SLAM employs an Extended Kalman Filter (EKF) to tightly fuse Inertial Measurement Unit (IMU) and wheel odometry, effectively compensating for vibration-induced inertial drift and wheel slippage during rotational motion. The EKF fusion output is then incorporated as a prior in a nonlinear optimization back-end together with RGB-D visual constraints, enabling accurate and globally consistent pose estimation. Extensive experiments demonstrate that MFL-SLAM achieves a 47.3 % reduction in relative pose error compared to ORB-SLAM3 and reduces the average localization error to 0.29 m, outperforming ORB-SLAM2 and LIO-SAM across small- and large-scale indoor environments. These results indicate that the proposed fusion strategy provides a robust and deployable solution for reliable indoor mobile robot localization.