A Sequential Fusion Localization Algorithm Based on AUKF for UWB/IMU/ODOM Systems

To address the positioning problem of Ultra-Wideband (UWB) in complex dynamic environments, this paper proposes a UWB/ Inertial Measurement Units (IMU)/Odometry (ODOM) fusion algorithm based on the Adaptive Unscented Kalman Filter (AUKF). By designing an independent measurement update module, centralized sequential fusion of UWB and trajectory estimation is achieved. This approach handles time-asynchronous data flexibly and introduces an adaptive noise adjustment mechanism and Mahalanobis distance-based outlier removal strategy, enhancing the system's dynamic adaptability and state estimation accuracy. Experimental results show that the proposed algorithm reduces the root mean square error by approximately 22.1% and 24.1% compared to UWB and trajectory estimation, respectively, and can perform continuous localization even when UWB data is interfered with or fails.