Philipp Fleck, Clemens Arth, Christian Pirchheim, D. Schmalstieg
{"title":"[POSTER] Tracking and Mapping with a Swarm of Heterogeneous Clients","authors":"Philipp Fleck, Clemens Arth, Christian Pirchheim, D. Schmalstieg","doi":"10.1109/ISMAR.2015.40","DOIUrl":null,"url":null,"abstract":"In this work, we propose a multi-user system for tracking and mapping, which accommodates mobile clients with different capabilities, mediated by a server capable of providing real-time structure from motion. Clients share their observations of the scene according to their individual capabilities. This can involve only keyframe tracking, but also mapping and map densification, if more computational resources are available. Our contribution is a system architecture that lets heterogeneous clients contribute to a collaborative mapping effort, without prescribing fixed capabilities for the client devices. We investigate the implications that the clients' capabilities have on the collaborative reconstruction effort and its use for AR applications.","PeriodicalId":240196,"journal":{"name":"2015 IEEE International Symposium on Mixed and Augmented Reality","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Symposium on Mixed and Augmented Reality","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISMAR.2015.40","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this work, we propose a multi-user system for tracking and mapping, which accommodates mobile clients with different capabilities, mediated by a server capable of providing real-time structure from motion. Clients share their observations of the scene according to their individual capabilities. This can involve only keyframe tracking, but also mapping and map densification, if more computational resources are available. Our contribution is a system architecture that lets heterogeneous clients contribute to a collaborative mapping effort, without prescribing fixed capabilities for the client devices. We investigate the implications that the clients' capabilities have on the collaborative reconstruction effort and its use for AR applications.