{"title":"Iterative smoothing approach using Gaussian mixture models for nonlinear estimation","authors":"Daniel J. Lee, M. Campbell","doi":"10.1109/IROS.2012.6385752","DOIUrl":null,"url":null,"abstract":"An iterative smoothing algorithm is developed using Gaussian mixture models in order to tackle challenging nonlinear estimation problems. Gaussian mixture models naturally capture nonlinear and non-Gaussian systems, while smoothing algorithms provide ability to update using measurements obtained in the past. A tree structure and Gaussian distribution splitting method are proposed to mitigate nonlinearity effects and complexities. Two methods, Children Collapsing and Parent Splitting, are developed to utilize sigma-points smoother for Gaussian mixture model. An indoor localization problem is used to explore and validate the approach. Performance of these new methods is compared to a baseline sigma-points smoother, in both simulation and experiment, and shows much improvement in overall error compared to the truth.","PeriodicalId":6358,"journal":{"name":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"74 1","pages":"2498-2503"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IROS.2012.6385752","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
An iterative smoothing algorithm is developed using Gaussian mixture models in order to tackle challenging nonlinear estimation problems. Gaussian mixture models naturally capture nonlinear and non-Gaussian systems, while smoothing algorithms provide ability to update using measurements obtained in the past. A tree structure and Gaussian distribution splitting method are proposed to mitigate nonlinearity effects and complexities. Two methods, Children Collapsing and Parent Splitting, are developed to utilize sigma-points smoother for Gaussian mixture model. An indoor localization problem is used to explore and validate the approach. Performance of these new methods is compared to a baseline sigma-points smoother, in both simulation and experiment, and shows much improvement in overall error compared to the truth.