UWB–IMU–Odometer Fusion for Simultaneous Calibration and Localization

The location accuracy of fixed anchors plays a pivotal role in ultrawideband (UWB) positioning. However, existing calibration methods for calculating the anchor positions require anchor-to-anchor communication or known initial values for anchor locations. Moreover, the calibration accuracy is adversely affected by non line-of-sight (NLOS) conditions. We propose a two-stage calibration scheme to conduct simultaneous calibration and localization (SCAL) based on UWB-inertial measurement unit (IMU)–odometer sensor fusion without the need for anchor-to-anchor communication or manual intervention. Our method performs IMU-odometer-aided multidimensional scaling (IO-MDS) to provide the initial calibration value without anchor-to-anchor ranging measurements. This is followed by a novel factor-graph-based framework to achieve coarse-to-fine calibration based on UWB, inertial, and odometer measurements. In existing works, a single UWB range measurement is regarded as a weak constraint, as it often leads to incorrect estimates. Our method uses the derived radial velocity (DRV) and IO-MDS factors as additional strong constraints for reliable estimates. To minimize the NLOS influence on the calibration process, we introduce an improved least square-support vector machine (ILS-SVM) based on adaptive weight parameter and multikernel function. Experimental results on two field collected datasets show enhancements in NLOS identification and SCAL. In the lab dataset, identification accuracy increased by 5.5%, with improvements of 0.124 m and 0.309 m in root mean square error (RMSE) for robot and anchor locations, respectively. In the parking lot dataset, identification accuracy improved by 5.0%, with RMSE improvements of 0.223 m and 0.317 m for robot and anchor locations, respectively.