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Keywords = robot kidnap recovery

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17 pages, 2179 KiB  
Article
UAPF: A UWB Aided Particle Filter Localization For Scenarios with Few Features
by Yang Wang, Weimin Zhang, Fangxing Li, Yongliang Shi, Fuyu Nie and Qiang Huang
Sensors 2020, 20(23), 6814; https://doi.org/10.3390/s20236814 - 28 Nov 2020
Cited by 7 | Viewed by 2345
Abstract
Lidar-based localization doesn’t have high accuracy in open scenarios with few features, and behaves poorly in robot kidnap recovery. To address this problem, an improved Particle Filter localization is proposed who could achieve robust robot kidnap detection and pose error compensation. UAPF adaptively [...] Read more.
Lidar-based localization doesn’t have high accuracy in open scenarios with few features, and behaves poorly in robot kidnap recovery. To address this problem, an improved Particle Filter localization is proposed who could achieve robust robot kidnap detection and pose error compensation. UAPF adaptively updates the covariance by Jacobian from Ultra-wide Band information instead of predetermined parameters, and determines whether robot kidnap occurs by a novel criterion called KNP (Kidnap Probability). Besides, pose fusion of ranging-based localization and PF-based localization is conducted to decrease the uncertainty. To achieve more accurate ranging-based localization, linear regression of ranging data adopts values of maximum probability rather than average distances. Experiments show UAPF can achieve robot kidnap recovery in less than 2 s and position error is less than 0.1 m in a hall of 40 by 15 m, when the currently prevalent lidar-based localization costs more than 90 s and converges to wrong position. Full article
(This article belongs to the Section Sensors and Robotics)
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