Geo-Localization from Images and 2D Maps

We present a method for large-scale geo-localization and global tracking of mobile devices in urban outdoor environments. In contrast to existing methods, we instantaneously initialize and globally register a SLAM map by localizing the first keyframe with respect to widely available untextured 2.5D maps.

Given a single image frame and a coarse sensor pose prior, our localization method estimates the absolute camera orientation from straight line segments and the translation by aligning the city map model with a semantic segmentation of the image. We use the resulting 6DOF pose, together with information inferred from the city map model, to reliably initialize and extend a 3D SLAM map in a global referential, applying a model-supported SLAM mapping approach.

We show the robustness and accuracy of our localization approach on a challenging dataset, and demonstrate unconstrained global SLAM mapping and tracking of arbitrary camera motion on several sequences.



Visionary Collaborative Outdoor Reconstruction using SLAM and SfM
Philipp Fleck, Dieter Schmalstieg, and Clemens Arth
In Software Engineering and Architectures for Realtime Interactive Systems Workshop, 2016.

Instant Outdoor Localization and SLAM Initialization from 2.5D Maps

Clemens Arth, Christian Pirchheim, Jonathan Ventura, Dieter Schmalstieg, and Vincent Lepetit
In Proceedings of the International Symposium on Mixed and Augmented Reality, 2015.

Team Lepetit


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