Torsten Sattler presents Challenges in Long-Term Visual Localization

On 2018-07-24 11:00:00 at CIIRC Seminar. Room B-670 (Building B, the 6th floor), Jugoslavskych partyzanu 3
*** CIIRC Seminar. Room B-670 (Building B, the 6th floor), Jugoslavskych
partyzanu 3 ***

Visual localization is the problem of estimating the position and orientation
from which an image was taken with respect to a 3D model of a known scene. This
problem has important applications, including autonomous vehicles (including
self-driving cars and other robots) and augmented / mixed / virtual reality.
While multiple solutions to the visual localization problem exist both in the
Robotics and Computer Vision communities for accurate camera pose estimation,
they typically assume that the scene does not change over time. However, this
assumption is often invalid in practice, both in indoor and outdoor
environments. This talk thus briefly discusses the challenges encountered when
trying to localize images over a longer period of time. Next, we show how a
combination of 3D scene geometry and higher-level scene understanding can help
to enable visual localization in conditions where both classical and recently
proposed learning-based approaches struggle.

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
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.
Za obsah zodpovídá: Petr Pošík