ArticleSensors (Basel, Switzerland)2020
UAPF: A UWB Aided Particle Filter Localization For Scenarios with Few Features.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- A Magnetic Crawler System for Autonomous Long-Range Inspection and Maintenance on Large Structures.Sensors (Basel, Switzerland) · 2022Article
- Novel Solutions to the Three-Anchor ToA-Based Three-Dimensional Positioning Problem.Sensors (Basel, Switzerland) · 2021Article
- Distance Measurements in UWB-Radio Localization Systems Corrected with a Feedforward Neural Network Model.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
6 authors.
Funding
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 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.
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Registered trials
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