Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Geometry-Aware Learning of Maps for Camera Localization   Maps are a key component in image-based camera localization and visual SLAM
systems: they are used to establish geometric constraints between images,
correct drift in relative pose estimation, and relocalize cameras after lost
tracking. The exact definitions of maps, however, are often
application-specific and hand-crafted for different scenarios (e.g. 3D
landmarks, lines, planes, bags of visual words). We propose to represent maps
as a deep neural net called MapNet, which enables learning a data-driven map
representation. Unlike prior work on learning maps, MapNet exploits cheap and
ubiquitous sensory inputs like visual odometry and GPS in addition to images
and fuses them together for camera localization. Geometric constraints
expressed by these inputs, which have traditionally been used in bundle
adjustment or pose-graph optimization, are formulated as loss terms in MapNet
training and also used during inference. In addition to directly improving
localization accuracy, this allows us to update the MapNet (i.e., maps) in a
self-supervised manner using additional unlabeled video sequences from the
scene. We also propose a novel parameterization for camera rotation which is
better suited for deep-learning based camera pose regression. Experimental
results on both the indoor 7-Scenes dataset and the outdoor Oxford RobotCar
dataset show significant performance improvement over prior work. The MapNet
project webpage is https://goo.gl/mRB3Au.

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The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int