An Inertial, Magnetic and Vision Based Trusted Pose Estimation for AR and 3D Data Qualification on Long Urban Pedestrian Displacements

被引：1

作者：

Antigny, Nicolas ^{[1
]}

Servieres, Myriam ^{[2
]}

Renaudin, Valerie ^{[3
]}

机构：

[1] Ecole Cent Nantes, IFSTTAR GEOLOC IRSTV, Nantes, France

[2] Ecole Cent Nantes, CRENAU AAU IRSTV, Nantes, France

[3] IFSTTAR GEOLOC IRSTV, Nantes, France

来源：

ADJUNCT PROCEEDINGS OF THE 2017 IEEE INTERNATIONAL SYMPOSIUM ON MIXED AND AUGMENTED REALITY (ISMAR-ADJUNCT) | 2017年

关键词：

Outdoor Augmented Reality; mobile device; Geographic Information Systems; city visualization;

D O I：

10.1109/ISMAR-Adjunct.2017.57

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In the context of pedestrian navigation, urban environment constitutes a challenging area for both localization and Augmented Reality (AR). In order to display 3D Geographic Information System (GIS) content in AR and to qualify them, we propose to fuse the pose estimated using vision thanks to a precisely known 3D urban furniture model with rotation estimated from inertial and magnetic measurements. An acquisition conducted in urban environment on a long pedestrian path permits to validate the contribution of sensors fusion and allows to qualify the pose estimation needed for AR 3D GIS content characterization.

引用

页码：168 / 169

页数：2

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