Pedestrian Localization with Stride-Wise Error Estimation and Compensation by Fusion of UWB and IMU Data

Abstract

Indoor positioning enables mobile machines to perform tasks (semi-)automatically, such as following an operator. However, the usefulness and safety of these applications depends on the reliability of the estimated operator localization. Thus, quantifying the accuracy of positioning at runtime is critical for the application in real-world industrial contexts. In this paper, we present a method that produces an estimate of the current positioning error for each user stride. To accomplish this, we construct a virtual stride vector from Ultra-Wideband (UWB) position measurements. The virtual vectors are then compared to stride vectors from a foot-mounted Inertial Measurement Unit (IMU). Using these independent measurements, we estimate the current reliability of the UWB measurements. Positioning errors are mitigated through loosely coupled filtering of both vector types. We evaluate our method in three environments, showing that it improves positioning accuracy, especially in challenging conditions with obstructed line of sight and sparse UWB infrastructure. Additionally, we demonstrate the mitigation of simulated spoofing attacks on UWB positioning. Our findings indicate that positioning quality can be judged at runtime by comparing user strides reconstructed from UWB and IMU measurements. Our method is independent of situation- or environment-specific parameter tuning, and as such represents a promising approach for detecting both known and unknown positioning error states.

Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects

“…Data from inertial sensors and the UWB system are commonly fused in multiple ways using KF. In [47], the authors presented an interesting method that combines UWB and pedestrian dead reckoning (PDR) in different conditions of the density of UWB anchors and different visibility of positioning pedestrians. They prepared two Kalman filters for orientation and position, using dynamic and fixed variance estimation.…”