3D Accuracy Assessment of an Indoor UWB Positioning System for UAV Emergency Applications Using VICON Ground Truth

Abstract

In recent years, amid the growing frequency of natural disasters and large-scale emergency scenarios, unmanned aerial vehicles (UAVs) have become essential tools for executing risky missions, without jeopardizing the lives of search and rescue personnel. While GNSS represents the most mature and robust positioning method, it becomes unreliable in indoor scenarios, as it is affected by multipath and non-line-of-sight (NLOS) conditions. This paper evaluates the 3D positioning accuracy of an ultra-wideband (UWB) indoor positioning system. The main purpose of this assessment is to decide whether the system can assure robust UAV positioning during emergency situations in GNSS-denied areas. The proposed system is rapid to deploy, cost-effective, and requires minimal hardware and software configuration, making it suitable for emergency response scenarios. The 3D trajectory estimated by the UWB positioning system is evaluated against ground truth positioning data provided by a VICON motion capture system. The system is tested in an uncontrolled indoor environment, with obstacles and NLOS conditions. In order to produce the best possible trajectory estimate, offline filtering techniques such as Kalman Filter, Savitzky-Golay filter and Rauch–Tung–Striebel smoothing are applied. The main purpose of these filters is to mitigate noise and the impact of outliers, and obtain a smoother and more reliable trajectory. A comparative analysis of Kalman Filter, Savitzky-Golay filter and Rauch–Tung–Striebel smoothing is presented by computing the Root Mean Square Error (RMSE) on each axis between the UWB measurements and the ground truth trajectory both before and after applying the offline filtering techniques.