Object-based Visual SLAM: How Object Identity Informs Geometry
Author(s)
Selvatici, Antonio H. P.
Costa, Anna H. R.
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Abstract
Objects are rich information sources about the
environment. A 3D model of the objects, together
with their semantic labels, can be used for camera
localization as well as for cognitive reasoning about
the environment. However, traditional frameworks
for scene reconstruction usually map a cloud of
points using structure-from-motion techniques, but
do not provide objects representation. On the other
side, robotics object-based mapping mainly focus
on adding cognitive representations to a metric or
topologic map built using traditional SLAM techniques.
In this work we propose a framework for
environment modeling by representing the objects in
the scene, detected by an object recognition and segmentation
technique. The key idea is to incorporate
the resulting image segments and labels into a global
inference engine in order to build simple geometric
models for the objects. For now, we consider the
perfect object recognition case, where we know the
exact object identities, testing our approach using
coarsely hand-annotated images captured by a robot
carrying an omnidirectional camera. We found that
the resultant object locations and sizes are fully
compatible with what is expected, and the inferred
robot trajectory is improved when compared to that
recovered using odometry only.
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Date
2008-11
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Text
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Post-print