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A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot
This paper proposes a moving-target tracking algorithm that measures the pose of a micro-robot with high precision and high speed using the Kalman filter-based kernelized correlation filter (K2CF) algorithm. The adaptive Kalman filter can predict the state of linearly and nonlinearly fast-moving tar...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306500/ https://www.ncbi.nlm.nih.gov/pubmed/34209055 http://dx.doi.org/10.3390/mi12070774 |
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author | Zhang, Heng Zhan, Hongwu Zhang, Libin Xu, Fang Ding, Xinbin |
author_facet | Zhang, Heng Zhan, Hongwu Zhang, Libin Xu, Fang Ding, Xinbin |
author_sort | Zhang, Heng |
collection | PubMed |
description | This paper proposes a moving-target tracking algorithm that measures the pose of a micro-robot with high precision and high speed using the Kalman filter-based kernelized correlation filter (K2CF) algorithm. The adaptive Kalman filter can predict the state of linearly and nonlinearly fast-moving targets. The kernelized correlation filter algorithm then accurately detects the positions of the moving targets and uses the detection results to modify the moving states of the targets. This paper verifies the performance of the algorithm on a monocular vision measurement platform and using a pose measurement method. The K2CF algorithm was embedded in the micro-robot’s attitude measurement system, and the tracking performances of three different trackers were compared under different motion conditions. Our tracker improved the positioning accuracy and maintained real-time operation. In a comparison study of K2CF and many other algorithms on Object Tracking Benchmark-50 and Object Tracking Benchmark-100 video sequences, the K2CF algorithm achieved the highest accuracy. In the 400 mm × 300 mm field of view, when the target radius is about 3 mm and the inter-frame acceleration displacement does not exceed 5.6 mm, the root-mean-square error of position and attitude angle can satisfy the precision requirements of the system. |
format | Online Article Text |
id | pubmed-8306500 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83065002021-07-25 A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot Zhang, Heng Zhan, Hongwu Zhang, Libin Xu, Fang Ding, Xinbin Micromachines (Basel) Article This paper proposes a moving-target tracking algorithm that measures the pose of a micro-robot with high precision and high speed using the Kalman filter-based kernelized correlation filter (K2CF) algorithm. The adaptive Kalman filter can predict the state of linearly and nonlinearly fast-moving targets. The kernelized correlation filter algorithm then accurately detects the positions of the moving targets and uses the detection results to modify the moving states of the targets. This paper verifies the performance of the algorithm on a monocular vision measurement platform and using a pose measurement method. The K2CF algorithm was embedded in the micro-robot’s attitude measurement system, and the tracking performances of three different trackers were compared under different motion conditions. Our tracker improved the positioning accuracy and maintained real-time operation. In a comparison study of K2CF and many other algorithms on Object Tracking Benchmark-50 and Object Tracking Benchmark-100 video sequences, the K2CF algorithm achieved the highest accuracy. In the 400 mm × 300 mm field of view, when the target radius is about 3 mm and the inter-frame acceleration displacement does not exceed 5.6 mm, the root-mean-square error of position and attitude angle can satisfy the precision requirements of the system. MDPI 2021-06-30 /pmc/articles/PMC8306500/ /pubmed/34209055 http://dx.doi.org/10.3390/mi12070774 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Heng Zhan, Hongwu Zhang, Libin Xu, Fang Ding, Xinbin A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot |
title | A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot |
title_full | A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot |
title_fullStr | A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot |
title_full_unstemmed | A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot |
title_short | A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot |
title_sort | kalman filter-based kernelized correlation filter algorithm for pose measurement of a micro-robot |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306500/ https://www.ncbi.nlm.nih.gov/pubmed/34209055 http://dx.doi.org/10.3390/mi12070774 |
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