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 IMUUWBESKF, 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.
He et al. (Sun,) studied this question.