An Indoor Multi-sensor Fusion Localization Method Based on UWB

Introduction/Objective: Global navigation satellite system (GNSS) localization accuracy is significantly degraded in indoor environments, failing to satisfy the precise localization requirements of robotic systems. Ultra-wideband (UWB) localization technology has been applied to indoor navigation. However, its accuracy degrades substantially in non-line-of-sight (NLOS) environments. To enhance the indoor localization accuracy of intelligent vehicles, this paper proposes a multi-sensor fusion localization method based on UWB, an inertial measurement unit (IMU), and light detection and ranging (LiDAR). Methods: The extended Kalman filter (EKF) is employed to construct separate UWB/IMU and UWB/LiDAR subsystems. The localization results of these two subsystems are subsequently optimized and corrected using the weight-adaptive particle filter (WAPF) algorithm to produce optimal localization state estimates. results: We conducted experiments using an intelligent vehicle in the non-line-of-sight environment of 4.2m×2.4m and the line-of-sight environment of 6.0m×1.0m respectively. In the non-line-of-sight environment, the average localization error of the method proposed in this paper reaches 0.0420m, which is 59.70% smaller than that of the single-sensor system and 18.52% smaller than that of the EKF method. In the line-of-sight environment, the average localization error of the method proposed in this paper reaches 0.0209m, which is 44.71% smaller than that of the single-sensor system and 14.34% smaller than that of the EKF method. Results: Experiments were conducted using an intelligent vehicle in a non-line-of-sight (NLOS) environment measuring 4.2 m × 2.4 m and a line-of-sight (LOS) environment measuring 6.0 m × 1.0 m. In the NLOS environment, the proposed method achieved an average localization error of 0.0420 m, which is 59.70% lower than that of the single-sensor system and 18.52% lower than that of the EKF method. In the LOS environment, the average localization error was reduced to 0.0209 m, representing a 44.71% reduction compared to the single-sensor system and a 14.34% reduction compared to the EKF method. Discussion: Data from the UWB/IMU and UWB/LiDAR subsystems were processed using the EKF and subsequently fused through the WAPF algorithm, achieving robust localization in indoor environments. Conclusion: The proposed multi-sensor fusion localization method effectively combines the continuous motion perception of the IMU, the environmental feature matching capability of LiDAR, and the long-range absolute position constraints provided by UWB. This integration successfully compensates for the shortcomings of individual sensors and improves the accuracy and stability of localization.