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An Information Gain Formulation for Active Volumetric 3D Reconstruction


Isler, Stefan; Sabzevari, Reza; Delmerico, Jeffrey; Scaramuzza, Davide (2016). An Information Gain Formulation for Active Volumetric 3D Reconstruction. In: IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 16 May 2016 - 21 May 2016. Institute of Electrical and Electronics Engineers, 3477-3484.

Abstract

We consider the problem of next-best view selection for volumetric reconstruction of an object by a mobile robot equipped with a camera. Based on a probabilistic volumetric map that is built in real time, the robot can quantify the expected information gain from a set of discrete candidate views. We propose and evaluate several formulations to quantify this information gain for the volumetric reconstruction task, including visibility likelihood and the likelihood of seeing new parts of the object. These metrics are combined with the cost of robot movement in utility functions. The next best view is selected by optimizing these functions, aiming to maximize the likelihood of discovering new parts of the object. We evaluate the functions with simulated and real world experiments within a modular software system that is adaptable to other robotic platforms and reconstruction problems. We release our implementation open source.

Abstract

We consider the problem of next-best view selection for volumetric reconstruction of an object by a mobile robot equipped with a camera. Based on a probabilistic volumetric map that is built in real time, the robot can quantify the expected information gain from a set of discrete candidate views. We propose and evaluate several formulations to quantify this information gain for the volumetric reconstruction task, including visibility likelihood and the likelihood of seeing new parts of the object. These metrics are combined with the cost of robot movement in utility functions. The next best view is selected by optimizing these functions, aiming to maximize the likelihood of discovering new parts of the object. We evaluate the functions with simulated and real world experiments within a modular software system that is adaptable to other robotic platforms and reconstruction problems. We release our implementation open source.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not_refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Software
Physical Sciences > Control and Systems Engineering
Physical Sciences > Artificial Intelligence
Physical Sciences > Electrical and Electronic Engineering
Scope:Discipline-based scholarship (basic research)
Language:English
Event End Date:21 May 2016
Deposited On:19 Jul 2016 07:27
Last Modified:06 Mar 2024 14:21
Publisher:Institute of Electrical and Electronics Engineers
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/ICRA.2016.7487527
Other Identification Number:merlin-id:13325