Autonomous systems have to understand the 3D space, being able to detect objects
and infer their pose to pick them and reliably perform a certain goal-oriented action.
An increasing number of works focus on this topic motivated by self-driving cars
or the Amazon picking challenge. In particular, we focus on pose estimation, a
well-known problem in computer vision and robotics which is essential for object
manipulation. This requires reliable identification of object poses to know how to
pick ...
Autonomous systems have to understand the 3D space, being able to detect objects
and infer their pose to pick them and reliably perform a certain goal-oriented action.
An increasing number of works focus on this topic motivated by self-driving cars
or the Amazon picking challenge. In particular, we focus on pose estimation, a
well-known problem in computer vision and robotics which is essential for object
manipulation. This requires reliable identification of object poses to know how to
pick them up in order to interact with them for a certain goal. Additionally, pose
estimation involves several difficulties. Objects may have rotational symmetries or
their appearance can vary significantly depending on the lighting or occlusions.
A common approach to pose estimation is to first estimate a coarse pose to initialize
ICP and get a fine pose estimation. We follow this idea in this work by
comparing an object in an RGB-D setting to a set of views of the same CAD model
obtained offline. Using Convolutional Neural Networks, we embed the images to a
common space where they can be efficiently compared. Additionally, we propose to
consider symmetries directly in the comparison to avoid inconsistencies in the pose
estimation.
Given the lack of benchmarks with symmetric objects for pose estimation, we obtain
6669 CAD models of very different kinds and generate realistic simulations of tabletop
scenarios to train and test our approach. We also leverage a non-published
dataset of real objects with symmetries. Finally, we infer rotational symmetries in
new CAD models, obtaining a high recall and promising results that suggest further
research.
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