Enhanced Indoor Mobile Robot Localization via Lie-Group IMU-UWB Fusion and Dual-Stage Kalman Filtering

Indoor mobile robots often experience degraded localization accuracy and robustness when relying on a single positioning modality. In addition, conventional pose computation based on Euler-parameterized transformations can be computationally involved and susceptible to singularities, while practical fusion schemes may not adequately suppress measurement errors. This paper proposes an indoor robot localization method, termed IMU_UWB_ESKF, which tightly fuses inertial and UWB measurements using a Lie-group state representation. IMU- and UWB-derived quantities are formulated on the associated Lie algebra, enabling numerically stable pose propagation and measurement updates. To mitigate sensor noise and reduce drift, a dual-stage Kalman filtering strategy is adopted: an EKF-based measurement correction is first performed, followed by a multi-dimensional error-state Kalman filter for refined fusion. The proposed pipeline is implemented on a wheeled-robot platform under ROS, integrating real-time IMU/UWB parameter extraction, pose transformation, and online state estimation. Experimental results demonstrate stable real-time localization with improved robustness and accuracy under dynamic motion, indicating the method's applicability to indoor navigation tasks.