Aashish Sheshadri, K. Peterson, H. Jones, W. Whittaker
{"title":"Position estimation by registration to planetary terrain","authors":"Aashish Sheshadri, K. Peterson, H. Jones, W. Whittaker","doi":"10.1109/MFI.2012.6343004","DOIUrl":null,"url":null,"abstract":"LIDAR-only and camera-only approaches to global localization in planetary environments have relied heavily on availability of elevation data. The low-resolution nature of available DEMs limits the accuracy of these methods. Availability of new high-resolution planetary imagery motivates the rover localization method presented here. The method correlates terrain appearance with orthographic imagery. A rover generates a colorized 3D model of the local terrain using a panorama of camera and LIDAR data. This model is orthographically projected onto the ground plane to create a template image. The template is then correlated with available satellite imagery to determine rover location. No prior elevation data is necessary. Experiments in simulation demonstrate 2m accuracy. This method is robust to 30° differences in lighting angle between satellite and rover imagery.","PeriodicalId":103145,"journal":{"name":"2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MFI.2012.6343004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
LIDAR-only and camera-only approaches to global localization in planetary environments have relied heavily on availability of elevation data. The low-resolution nature of available DEMs limits the accuracy of these methods. Availability of new high-resolution planetary imagery motivates the rover localization method presented here. The method correlates terrain appearance with orthographic imagery. A rover generates a colorized 3D model of the local terrain using a panorama of camera and LIDAR data. This model is orthographically projected onto the ground plane to create a template image. The template is then correlated with available satellite imagery to determine rover location. No prior elevation data is necessary. Experiments in simulation demonstrate 2m accuracy. This method is robust to 30° differences in lighting angle between satellite and rover imagery.