Mobile Robot Localization Based on Gradient Propagation Particle Filter Network

In order to solve the problem that the gradient information can’t propagate backward due to the non-differentiability of resampling process in the end-to-end training of Differentiable Particle Filters (DPFs) network model, a particle filter network with gradient propagation is proposed i...

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Autores principales:	Heng Zhang, Jiemao Wen, Yanli Liu, Wenqing Luo, Naixue Xiong
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Robot localization particle filter deep neural network state estimation Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/063ce98188f84191bb67566055a282ff
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spelling	oai:doaj.org-article:063ce98188f84191bb67566055a282ff2021-11-19T00:05:48ZMobile Robot Localization Based on Gradient Propagation Particle Filter Network2169-353610.1109/ACCESS.2020.3031618https://doaj.org/article/063ce98188f84191bb67566055a282ff2020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9226533/https://doaj.org/toc/2169-3536In order to solve the problem that the gradient information can’t propagate backward due to the non-differentiability of resampling process in the end-to-end training of Differentiable Particle Filters (DPFs) network model, a particle filter network with gradient propagation is proposed in this article. It uses a differentiable resampling process in the network and is called Gradient Propagation Particle Filter Network(GPPFN). Firstly, an auxiliary noise vector is added to approximate the particle distribution in the resampling process, so that it can be represented by a certain differentiable function. Then, the method is applied to GPPFN model to make the gradient information generated in the training process can be propagated. It can provide the information of previous prediction and update steps for the next step prediction, so as to improve the accuracy of robot localization. In this article, experiments are carried out in the simulation environment of Deepmind lab. GPPFN is used to train the motion model and measurement model respectively, and the training effect is expressed by likelihood degree. The lower the likelihood degree is, the more consistent the training results are. The experiments show that the likelihood loss of motion model and measurement model are reduced by using GPPFN. Finally, The GPPFN model is compared with other methods. The training results showed that compared with the others, the training results of GPPFN has lower error rate and better robustness.Heng ZhangJiemao WenYanli LiuWenqing LuoNaixue XiongIEEEarticleRobot localizationparticle filterdeep neural networkstate estimationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 188475-188487 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Robot localization particle filter deep neural network state estimation Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Robot localization particle filter deep neural network state estimation Electrical engineering. Electronics. Nuclear engineering TK1-9971 Heng Zhang Jiemao Wen Yanli Liu Wenqing Luo Naixue Xiong Mobile Robot Localization Based on Gradient Propagation Particle Filter Network
description	In order to solve the problem that the gradient information can’t propagate backward due to the non-differentiability of resampling process in the end-to-end training of Differentiable Particle Filters (DPFs) network model, a particle filter network with gradient propagation is proposed in this article. It uses a differentiable resampling process in the network and is called Gradient Propagation Particle Filter Network(GPPFN). Firstly, an auxiliary noise vector is added to approximate the particle distribution in the resampling process, so that it can be represented by a certain differentiable function. Then, the method is applied to GPPFN model to make the gradient information generated in the training process can be propagated. It can provide the information of previous prediction and update steps for the next step prediction, so as to improve the accuracy of robot localization. In this article, experiments are carried out in the simulation environment of Deepmind lab. GPPFN is used to train the motion model and measurement model respectively, and the training effect is expressed by likelihood degree. The lower the likelihood degree is, the more consistent the training results are. The experiments show that the likelihood loss of motion model and measurement model are reduced by using GPPFN. Finally, The GPPFN model is compared with other methods. The training results showed that compared with the others, the training results of GPPFN has lower error rate and better robustness.
format	article
author	Heng Zhang Jiemao Wen Yanli Liu Wenqing Luo Naixue Xiong
author_facet	Heng Zhang Jiemao Wen Yanli Liu Wenqing Luo Naixue Xiong
author_sort	Heng Zhang
title	Mobile Robot Localization Based on Gradient Propagation Particle Filter Network
title_short	Mobile Robot Localization Based on Gradient Propagation Particle Filter Network
title_full	Mobile Robot Localization Based on Gradient Propagation Particle Filter Network
title_fullStr	Mobile Robot Localization Based on Gradient Propagation Particle Filter Network
title_full_unstemmed	Mobile Robot Localization Based on Gradient Propagation Particle Filter Network
title_sort	mobile robot localization based on gradient propagation particle filter network
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/063ce98188f84191bb67566055a282ff
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_version_	1718420648814968832

Mobile Robot Localization Based on Gradient Propagation Particle Filter Network

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