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Torsten Sattler
presents
Camera Localization
 
On 2017-05-11 11:00
 
Estimating the position and orientation of a camera in a scene based on images
is an essential part of many (3D) Computer Vision and Robotics algorithms such
as Structure-from-Motion, Simultaneous Localization and Mapping (SLAM), and
visual localization. Camera localization has applications in navigation for
autonomous vehicles/robots, Augmented and Virtual Reality, and 3D mapping.
Furthermore, there are strong relations to camera calibration and visual place
recognition. In this talk, I will give an overview over past and current
efforts
on robust, efficient, and accurate camera localization. I will begin the talk
showing that classical localization approaches haven't been made obsolete by
deep learning. Following a local feature-based approach, the talk will discuss
how to adapt such methods for real-time visual localization on mobile devices
with limited computational capabilities and approaches that scale to large
(city-scale) scenes, including the challenges encountered at large-scale. The
final part of the talk will discuss open problems in the areas of camera
localization and 3D mapping, both in terms of problems we are currently working
on as well as interesting long-term goals.

Short bio:

Torsten Sattler received a PhD in Computer Science from RWTH Aachen University,
Germany, in 2013 under the supervision of Prof. Bastian Leibe and Prof. Leif
Kobbelt. In December 2013, he joined the Computer Vision and Geometry Group of
Prof. Marc Pollefeys at ETH Zurich, Switzerland, where he currently is a senior
researcher and Marc Pollefeys' deputy while Prof. Pollefeys is on leave from
ETH. His research interests include (large-scale) image-based localization
using
Structure-from-Motion point clouds, real-time localization and SLAM on mobile
devices and for robotics, 3D mapping, Augmented & Virtual Reality, (multi-view)
stereo, image retrieval and efficient spatial verification, camera calibration
and pose estimation. Torsten has worked on dense sensing for self-driving cars
as part of the V-Charge project. He is currently involved in enabling semantic
SLAM and re-localization for gardening robots (as part of a EU Horizon 2020
project where he leads the efforts on a workpackage), research for Google's
Tango project, where he leads CVG's research efforts, and in work on
self-driving cars.
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