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Tsinghua Science and Technology  2019, Vol. 24 Issue (06): 728-737    doi: 10.26599/TST.2018.9010102
REGULAR ARTICLES     
Anchor Self-Localization Algorithm Based on UWB Ranging and Inertial Measurements
Qin Shi, Sihao Zhao, Xiaowei Cui*, Mingquan Lu, Mengdi Jia
∙ Qin Shi, Sihao Zhao, Xiaowei Cui, Mingquan Lu, and Mengdi Jia are with the Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
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Abstract  

Localization systems utilizing Ultra-WideBand (UWB) have been widely used in dense urban and indoor environments. A moving UWB tag can be located by ranging to fixed UWB anchors whose positions are surveyed in advance. However, manually surveying the anchors is typically a dull and time-consuming process and prone to artificial errors. In this paper, we present an accurate and easy-to-use method for UWB anchor self-localization, using the UWB ranging measurements and readings from a low-cost Inertial Measurement Unit (IMU). The locations of the anchors are automatically estimated by freely moving the tag in the environment. The method is inspired by the Simultaneous Localization And Mapping (SLAM) technique used by the robotics community. A tightly-coupled Error-State Kalman Filter (ESKF) is utilized to fuse UWB and inertial measurements, producing UWB anchor position estimates and six Degrees of Freedom (6DoF) tag pose estimates. Simulated experiments demonstrate that our proposed method enables accurate self-localization for UWB anchors and smooth tracking of the tag.



Key wordsanchor self-localization      error-state Kalman filter      sensor fusion      simultaneous localization and mapping     
Received: 16 April 2018      Published: 20 June 2019
Corresponding Authors: Xiaowei Cui   
About author:

Mingquan Lu is a professor of the Department of Electronic Engineering, Tsinghua University, China. He is the director of Tsinghua Position, Navigation and Timing Center, and a member of the Expert Group of China BeiDou Navigation Satellite System. His current research interests include GNSS signal design and analysis, GNSS signal processing and receiver development, and GNSS system modeling and simulation. He received the MS and PhD degrees from University of Electronic Science and Technology of China in 1993 and 1999, respectively.

Cite this article:

Qin Shi, Sihao Zhao, Xiaowei Cui, Mingquan Lu, Mengdi Jia. Anchor Self-Localization Algorithm Based on UWB Ranging and Inertial Measurements. Tsinghua Science and Technology, 2019, 24(06): 728-737.

URL:

http://tst.tsinghuajournals.com/10.26599/TST.2018.9010102     OR     http://tst.tsinghuajournals.com/Y2019/V24/I06/728

Fig. 1 A typical anchor self-localization scenario: when UWB anchors are newly deployed, they are self-localized by freely moving an IMU mounted UWB tag in the measurement volume.
Fig. 2 A double-sided two-way ranging exchange.
Fig. 3 Tightly-coupled fusion algorithm. Low rate UWB ranging measurements and high rate inertial measurements are directly used for simultaneous anchor self-localization and tag state estimation.
Fig. 4 Ground truth of positions of anchors and trajectory of the UAV. The red line is the simulated spiral trajectory and the blue six-pointed stars are positions of anchors.
xyz
Anchor 15.602.780.17
Anchor 23.521.510.22
Anchor 30.090.380.88
Anchor 41.925.321.00
Anchor 52.901.920.70
Table 1 Anchor self-localization errors. (cm)
Fig. 5 Self-localization results of UWB anchors. The estimated anchor positions are depicted in green.
Fig. 6 Self-localization results of the 5-th anchor and its comparison against the ground truth.
Fig. 7 UAV state estimates and their comparison against the ground truth.
Fig. 8 Performance of our method with respect to UWB ranging noise.
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