{"title":"基于粒子群算法的车辆定位研究","authors":"Jorge Godoy, D. Gruyer, A. Lambert, J. Villagrá","doi":"10.1109/IVS.2012.6232213","DOIUrl":null,"url":null,"abstract":"This paper describes the development of a filter algorithm based on the behaviour of biological swarms. The main goal of the algorithm is to perform vehicle localization by combining the data from different sensors - GPS, IMU, speedometers, etc. - and digital maps. In this sense, the algorithm considers several solutions at the same time like Particles Filters. The algorithm has been developed off-line using real data captured from an instrumented vehicle at LIVIC. Performance of the algorithm has been validated and compared with and EKF with encouraging results.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Development of an particle swarm algorithm for vehicle localization\",\"authors\":\"Jorge Godoy, D. Gruyer, A. Lambert, J. Villagrá\",\"doi\":\"10.1109/IVS.2012.6232213\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes the development of a filter algorithm based on the behaviour of biological swarms. The main goal of the algorithm is to perform vehicle localization by combining the data from different sensors - GPS, IMU, speedometers, etc. - and digital maps. In this sense, the algorithm considers several solutions at the same time like Particles Filters. The algorithm has been developed off-line using real data captured from an instrumented vehicle at LIVIC. Performance of the algorithm has been validated and compared with and EKF with encouraging results.\",\"PeriodicalId\":402389,\"journal\":{\"name\":\"2012 IEEE Intelligent Vehicles Symposium\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Intelligent Vehicles Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVS.2012.6232213\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Intelligent Vehicles Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2012.6232213","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development of an particle swarm algorithm for vehicle localization
This paper describes the development of a filter algorithm based on the behaviour of biological swarms. The main goal of the algorithm is to perform vehicle localization by combining the data from different sensors - GPS, IMU, speedometers, etc. - and digital maps. In this sense, the algorithm considers several solutions at the same time like Particles Filters. The algorithm has been developed off-line using real data captured from an instrumented vehicle at LIVIC. Performance of the algorithm has been validated and compared with and EKF with encouraging results.