Authors:
Daniel Kurz
1
;
Peter Georg Meier
1
;
Alexander Plopski
2
and
Gudrun Klinker
3
Affiliations:
1
metaio GmbH, Germany
;
2
Osaka University, Japan
;
3
Technische Universität München, Germany
Keyword(s):
Visual Feature Descriptors, Repetitiveness, Camera Localization, Inertial Sensors, Magnetometer, GPS.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image Registration
;
Motion, Tracking and Stereo Vision
;
Tracking and Visual Navigation
Abstract:
We present a framework that enables 6DoF camera localization in outdoor environments by providing visual
feature descriptors with an Absolute Spatial Context (ASPAC). These descriptors combine visual information
from the image patch around a feature with spatial information, based on a model of the environment and the
readings of sensors attached to the camera, such as GPS, accelerometers, and a digital compass. The result is a
more distinct description of features in the camera image, which correspond to 3D points in the environment.
This is particularly helpful in urban environments containing large amounts of repetitive visual features.
Additionally, we describe the first comprehensive test database for outdoor handheld camera localization comprising
of over 45,000 real camera images of an urban environment, captured under natural camera motions
and different illumination settings. For all these images, the dataset not only contains readings of the sensors
attached to the camera,
but also ground truth information on the full 6DoF camera pose, and the geometry
and texture of the environment. Based on this dataset, which we have made available to the public, we show
that using our proposed framework provides both faster matching and better localization results compared to
state-of-the-art methods.
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