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Authors: Mehdi Stapleton 1 ; Dieter Schmalstieg 2 ; Clemens Arth 1 and Thomas Gloor 3

Affiliations: 1 ICG, Graz University of Technology, Inffeldgasse 16/2, 8010 Graz, Austria, 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

ISBN: 978-989-758-402-2

ISSN: 2184-4321

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. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Stapleton, M.; Schmalstieg, D.; Arth, C. and Gloor, T. (2020). Learning Effective Sparse Sampling Strategies using Deep Active Sensing. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-402-2; ISSN 2184-4321, pages 835-846. DOI: 10.5220/0009172608350846

@conference{visapp20,
author={Mehdi Stapleton. and Dieter Schmalstieg. and Clemens Arth. and Thomas Gloor.},
title={Learning Effective Sparse Sampling Strategies using Deep Active Sensing},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2020},
pages={835-846},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009172608350846},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Learning Effective Sparse Sampling Strategies using Deep Active Sensing
SN - 978-989-758-402-2
IS - 2184-4321
AU - Stapleton, M.
AU - Schmalstieg, D.
AU - Arth, C.
AU - Gloor, T.
PY - 2020
SP - 835
EP - 846
DO - 10.5220/0009172608350846

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