{"title":"用于GNSS和相机辅助惯性导航的均匀半全局指数稳定非线性观测器","authors":"L. Fusini, T. Fossen, T. Johansen","doi":"10.1109/MED.2014.6961510","DOIUrl":null,"url":null,"abstract":"In this paper a nonlinear observer for estimation of position, velocity, acceleration, attitude and gyro bias of an Unmanned Aerial Vehicle (UAV) is proposed. The sensor suite consists of an Inertial Measurement Unit (IMU), a Global Navigation Satellite System (GNSS) receiver, a video camera, an altimeter, and an inclinometer. The camera and machine vision systems can track features from the environment and calculate the optical flow. These data, together with those from the other sensors, are fed to the observer, that is proven to be uniformly semiglobally exponentially stable (USGES). The performance of the observer is tested on simulated data by assuming that the camera system can provide the necessary information.","PeriodicalId":127957,"journal":{"name":"22nd Mediterranean Conference on Control and Automation","volume":"111 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"A uniformly semiglobally exponentially stable nonlinear observer for GNSS- and camera-aided inertial navigation\",\"authors\":\"L. Fusini, T. Fossen, T. Johansen\",\"doi\":\"10.1109/MED.2014.6961510\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper a nonlinear observer for estimation of position, velocity, acceleration, attitude and gyro bias of an Unmanned Aerial Vehicle (UAV) is proposed. The sensor suite consists of an Inertial Measurement Unit (IMU), a Global Navigation Satellite System (GNSS) receiver, a video camera, an altimeter, and an inclinometer. The camera and machine vision systems can track features from the environment and calculate the optical flow. These data, together with those from the other sensors, are fed to the observer, that is proven to be uniformly semiglobally exponentially stable (USGES). The performance of the observer is tested on simulated data by assuming that the camera system can provide the necessary information.\",\"PeriodicalId\":127957,\"journal\":{\"name\":\"22nd Mediterranean Conference on Control and Automation\",\"volume\":\"111 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"22nd Mediterranean Conference on Control and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MED.2014.6961510\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"22nd Mediterranean Conference on Control and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MED.2014.6961510","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A uniformly semiglobally exponentially stable nonlinear observer for GNSS- and camera-aided inertial navigation
In this paper a nonlinear observer for estimation of position, velocity, acceleration, attitude and gyro bias of an Unmanned Aerial Vehicle (UAV) is proposed. The sensor suite consists of an Inertial Measurement Unit (IMU), a Global Navigation Satellite System (GNSS) receiver, a video camera, an altimeter, and an inclinometer. The camera and machine vision systems can track features from the environment and calculate the optical flow. These data, together with those from the other sensors, are fed to the observer, that is proven to be uniformly semiglobally exponentially stable (USGES). The performance of the observer is tested on simulated data by assuming that the camera system can provide the necessary information.