Luis F. Contreras-Samame, Olivier Kermorgant, P. Martinet
{"title":"Efficient Decentralized Collaborative Mapping for Outdoor Environments","authors":"Luis F. Contreras-Samame, Olivier Kermorgant, P. Martinet","doi":"10.1109/IRC.2018.00017","DOIUrl":null,"url":null,"abstract":"An efficient mapping in mobile robotics may involve the participation of several agents. In this context, this article presents a framework for collaborative mapping applied to outdoor environments considering a decentralized approach. The mapping approach uses range measurements from a 3D lidar moving in six degrees of freedom. For that case, each robot performs a local SLAM. The maps are then merged when communication is available between the mobile units. This allows building a global map and to improve the state estimation of each agent. Experimental results are presented, where partial maps of the same environment are aligned and merged coherently in spite of the noise from the lidar measurement.","PeriodicalId":416113,"journal":{"name":"2018 Second IEEE International Conference on Robotic Computing (IRC)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Second IEEE International Conference on Robotic Computing (IRC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRC.2018.00017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
An efficient mapping in mobile robotics may involve the participation of several agents. In this context, this article presents a framework for collaborative mapping applied to outdoor environments considering a decentralized approach. The mapping approach uses range measurements from a 3D lidar moving in six degrees of freedom. For that case, each robot performs a local SLAM. The maps are then merged when communication is available between the mobile units. This allows building a global map and to improve the state estimation of each agent. Experimental results are presented, where partial maps of the same environment are aligned and merged coherently in spite of the noise from the lidar measurement.
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