Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Heng, Zhan, Hongwu, Zhang, Libin, Xu, Fang, Ding, Xinbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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
_version_ 1783727825876942848
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
work_keys_str_mv AT zhangheng akalmanfilterbasedkernelizedcorrelationfilteralgorithmforposemeasurementofamicrorobot
AT zhanhongwu akalmanfilterbasedkernelizedcorrelationfilteralgorithmforposemeasurementofamicrorobot
AT zhanglibin akalmanfilterbasedkernelizedcorrelationfilteralgorithmforposemeasurementofamicrorobot
AT xufang akalmanfilterbasedkernelizedcorrelationfilteralgorithmforposemeasurementofamicrorobot
AT dingxinbin akalmanfilterbasedkernelizedcorrelationfilteralgorithmforposemeasurementofamicrorobot
AT zhangheng kalmanfilterbasedkernelizedcorrelationfilteralgorithmforposemeasurementofamicrorobot
AT zhanhongwu kalmanfilterbasedkernelizedcorrelationfilteralgorithmforposemeasurementofamicrorobot
AT zhanglibin kalmanfilterbasedkernelizedcorrelationfilteralgorithmforposemeasurementofamicrorobot
AT xufang kalmanfilterbasedkernelizedcorrelationfilteralgorithmforposemeasurementofamicrorobot
AT dingxinbin kalmanfilterbasedkernelizedcorrelationfilteralgorithmforposemeasurementofamicrorobot