UWB-IMU-Odometer Fusion for Simultaneous Calibration and Localization

Jian Sun, Wei Sun, Jin Zheng, Xu Fang, Jian Liu, Ajmal Mian

Research output: Contribution to journalArticlepeer-review

Abstract

The location accuracy of fixed anchors plays a pivotal role in Ultra-wideband (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-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 DRV (derived radial velocity) 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 multi-kernel 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 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.

Original languageEnglish
Number of pages14
JournalIEEE Internet of Things Journal
Volume12
Issue number1
Early online date3 Oct 2024
DOIs
Publication statusPublished - 1 Jan 2025

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