Authors:
Mehdi Stapleton
1
;
2
;
Dieter Schmalstieg
2
;
Clemens Arth
1
;
2
and
Thomas Gloor
3
Affiliations:
1
AR4 GmbH, Strauchergasse 13, 8020 Graz, Austria
;
2
ICG, Graz University of Technology, Inffeldgasse 16/2, 8010 Graz, Austria
;
3
Hilti Corporation, Feldkircherstrasse 100, 9494 Schaan, Liechtenstein
Keyword(s):
Sparse Registration, Active Perception, Active Localization, General Hough Transform.
Abstract:
Registering a known model with noisy sample measurements is in general a difficult task due to the problem in
finding correspondences between the samples and points on the known model. General frameworks exist, such
as variants of the classical iterative closest point (ICP) method to iteratively refine correspondence estimates.
However, the methods are prone to getting trapped in locally optimal configurations, which may be far from
the true registration. The quality of the final registration depends strongly on the set of samples. The quality
of the set of sample measurements is more noticeable when the number of samples is relatively low (≈ 20).
We consider sample selection in the context of active perception, i.e. an objective-driven decision-making
process, to motivate our research and the construction of our system. We design a system for learning how
to select the regions of the scene to sample, and, in doing so, improve the accuracy and efficiency of the
sampling proc
ess. We present a full environment for learning how best to sample a scene in order to quickly
and accurately register a model with the scene. This work has broad applicability from the fields of geodesy to
medical robotics, where the cost of taking a measurement is much higher than the cost of incremental changes
to the pose of the equipment.
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