{"title":"使用可见区域的物理和虚拟无线电发射机匹配图","authors":"M. Ulmschneider, C. Gentner, A. Dammann","doi":"10.1109/PLANS46316.2020.9110139","DOIUrl":null,"url":null,"abstract":"Channel-SLAM is a multipath assisted positioning algorithm that treats multipath components as line-of-sight (LoS) signals from virtual transmitters. It maps the physical and virtual transmitters' locations simultaneously with estimating the user position with simultaneous localization and mapping (SLAM). Since Channel-SLAM is a relative localization system, the coordinate systems of transmitter maps from different users are subject to an unknown relative rotation and translation. In this paper, we present a new algorithm to estimate this rotation and translation, which we call map matching. Map matching is a requirement for collaborative Channel-SLAM, where users share transmitter maps to improve their positioning performance. Our idea is to augment maps of transmitter locations in Channel-SLAM with knowledge on from which locations there is a LoS condition to a transmitter in order to increase the robustness of map matching. We evaluate our algorithm by simulations in an indoor scenario.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Matching Maps of Physical and Virtual Radio Transmitters Using Visibility Regions\",\"authors\":\"M. Ulmschneider, C. Gentner, A. Dammann\",\"doi\":\"10.1109/PLANS46316.2020.9110139\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Channel-SLAM is a multipath assisted positioning algorithm that treats multipath components as line-of-sight (LoS) signals from virtual transmitters. It maps the physical and virtual transmitters' locations simultaneously with estimating the user position with simultaneous localization and mapping (SLAM). Since Channel-SLAM is a relative localization system, the coordinate systems of transmitter maps from different users are subject to an unknown relative rotation and translation. In this paper, we present a new algorithm to estimate this rotation and translation, which we call map matching. Map matching is a requirement for collaborative Channel-SLAM, where users share transmitter maps to improve their positioning performance. Our idea is to augment maps of transmitter locations in Channel-SLAM with knowledge on from which locations there is a LoS condition to a transmitter in order to increase the robustness of map matching. We evaluate our algorithm by simulations in an indoor scenario.\",\"PeriodicalId\":273568,\"journal\":{\"name\":\"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)\",\"volume\":\"108 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PLANS46316.2020.9110139\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PLANS46316.2020.9110139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Matching Maps of Physical and Virtual Radio Transmitters Using Visibility Regions
Channel-SLAM is a multipath assisted positioning algorithm that treats multipath components as line-of-sight (LoS) signals from virtual transmitters. It maps the physical and virtual transmitters' locations simultaneously with estimating the user position with simultaneous localization and mapping (SLAM). Since Channel-SLAM is a relative localization system, the coordinate systems of transmitter maps from different users are subject to an unknown relative rotation and translation. In this paper, we present a new algorithm to estimate this rotation and translation, which we call map matching. Map matching is a requirement for collaborative Channel-SLAM, where users share transmitter maps to improve their positioning performance. Our idea is to augment maps of transmitter locations in Channel-SLAM with knowledge on from which locations there is a LoS condition to a transmitter in order to increase the robustness of map matching. We evaluate our algorithm by simulations in an indoor scenario.