An Improved Robust ESKF Fusion Positioning Method with a Novel UWB-VIO Initialization.

Visual-inertial odometry (VIO) often struggles with illumination variations, sparse visual features, and inertial drift in complex indoor settings, leading to scale uncertainties and accumulated errors. To address these issues, this paper proposes a new UWB-VIO initialization method combined with an enhanced Robust error-state Kalman filter (Robust ESKF) fusion technique for mobile robot localization. During initialization, common problems include scale drift and heading inconsistency. To solve these, a direction-consistent constrained initialization model is developed. By jointly optimizing the scale factor and yaw angle, this model ensures consistent alignment between the visual-inertial and ultra-wideband (UWB) coordinate frames. This approach removes the need for external calibration and independent coordinate transformation, which are typically required by traditional methods. In the fusion process, an improved residual-weighted robust filtering mechanism is employed to minimize the impact of abnormal UWB ranging data and noise interference. This mechanism adaptively suppresses outliers caused by UWB multipath reflections and non-line-of-sight (NLOS) propagation, thereby reducing VIO drift and improving the overall robustness and stability of the localization system. Experiments conducted in narrow-corridor environments, where both UWB and visual sensors are affected by interference, demonstrate that the proposed method significantly reduces trajectory drift and attitude jumps, resulting in better positioning accuracy and trajectory continuity. Compared to conventional UWB-VIO fusion algorithms, the proposed method enhances average localization accuracy by over 50% and maintains stable estimation even in severe multipath interference conditions, demonstrating high precision and strong robustness.