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ITSC 2020 Paper and Releasing Q-Drone UWB Benchmark

Zahra Arjmandi (2nd year Ph.D. student), Dr. Jungwon Kang and Kunwoo Park (M.Sc. Student graduated in 2020) in the lab published her paper on a novel benchmark dataset for Ultra-wideband (UWB)-based positioning of the unmanned aerial vehicle (UAV) in the 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC 2020). She will present her research work at the conference virtually during the conference period starting from Sept. 20 ~ Sept. 23, 2020. The title of Zahra’s paper is “Benchmark dataset of Ultra-Wideband Radio Based UAV positioning”. The benchmark provides the five UWB datasets as well as IMU data using a small size of UAB. The dataset was collected in diverse types of environments including indoor air cargo, open soccer field, open site nearby building, semi-open tunnel and under the bridge. A reference position of Q-Drone was collected by a survey-grade total station with a laser tracker.


Benchmark dataset of Ultra-Wideband Radio Based UAV positioning

Abstract: Precise positioning of the Unmanned Aerial Vehicle (UAV) is critical to conduct many sophisticated civil and military applications in challenging environments. Many of the-state-of-the-art positioning methods rely on active range sensors. Among many available ranging sensors, Ultra-wideband (UWB) can provide many benefits such as high precision, power efficiency, and not prone to multipath propagation and noise. Thus, the UWB has recently been attracting many interests from the research community as a complementary positioning sensor. However, there is a significant lack of UWB benchmark data available to support developing, testing and generalizing their own positioning methods using UWB sensors. In this paper, we present a unique benchmark dataset that provides UWB and IMU signals acquired by a Q-Drone system in a diverse environment, including an indoor, open field, close to buildings, underneath the bridge, and semi-open tunnel, as shown in Fig.1. This benchmark also provides ground truth of UAV positions independently measured with robotic total stations. In this paper, we present the characteristics of the UWB benchmark dataset, Q-Drone UAV platform and the results of the quality assessment conducted by a baseline positioning algorithm implemented with multilateration principal and non-linear optimization.

A recorded oral presentation of the ITSC 2020 conference paper (Prepared by Zahra Armandi).

Q-Drone UWB Benchmark Website is available in the public domain through our benchmark website – https://benchmark.qdrone.ausmlab.com/. The website was implemented by Afnan Ahmad (Ph.D. Student) and Zahra Arjmandi.

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