{"title":"基于外观的分组LASSO回归定位与室内实验","authors":"Huan N. Do, Jongeun Choi, C. Lim, T. Maiti","doi":"10.1109/AIM.2015.7222667","DOIUrl":null,"url":null,"abstract":"This paper proposes appearance-based localization using online vision images collected from an omnidirectional camera attached on a mobile robot or a vehicle. Our approach builds on a combination of the group Least Absolute Shrinkage and Selection Operator (LASSO) and the extended Kalman filter (EKF). Fast Fourier transform (FFT) and Histogram are extracted from omni-directional images, the features of which are selected via the group LASSO regression. The EKF takes the output of the group LASSO regression based first-stage localization as the observation. The indoor experimental results demonstrate the effectiveness of our approach.","PeriodicalId":199432,"journal":{"name":"2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Appearance-based localization using Group LASSO regression with an indoor experiment\",\"authors\":\"Huan N. Do, Jongeun Choi, C. Lim, T. Maiti\",\"doi\":\"10.1109/AIM.2015.7222667\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes appearance-based localization using online vision images collected from an omnidirectional camera attached on a mobile robot or a vehicle. Our approach builds on a combination of the group Least Absolute Shrinkage and Selection Operator (LASSO) and the extended Kalman filter (EKF). Fast Fourier transform (FFT) and Histogram are extracted from omni-directional images, the features of which are selected via the group LASSO regression. The EKF takes the output of the group LASSO regression based first-stage localization as the observation. The indoor experimental results demonstrate the effectiveness of our approach.\",\"PeriodicalId\":199432,\"journal\":{\"name\":\"2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIM.2015.7222667\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIM.2015.7222667","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Appearance-based localization using Group LASSO regression with an indoor experiment
This paper proposes appearance-based localization using online vision images collected from an omnidirectional camera attached on a mobile robot or a vehicle. Our approach builds on a combination of the group Least Absolute Shrinkage and Selection Operator (LASSO) and the extended Kalman filter (EKF). Fast Fourier transform (FFT) and Histogram are extracted from omni-directional images, the features of which are selected via the group LASSO regression. The EKF takes the output of the group LASSO regression based first-stage localization as the observation. The indoor experimental results demonstrate the effectiveness of our approach.