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Aitor Aldoma
Researcher at Vienna University of Technology
Publications - 18
Citations - 1825
Aitor Aldoma is an academic researcher from Vienna University of Technology. The author has contributed to research in topics: Object (computer science) & Cognitive neuroscience of visual object recognition. The author has an hindex of 13, co-authored 18 publications receiving 1620 citations. Previous affiliations of Aitor Aldoma include University of Vienna.
Papers
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Journal ArticleDOI
Tutorial: Point Cloud Library: Three-Dimensional Object Recognition and 6 DOF Pose Estimation
Aitor Aldoma,Zoltan-Csaba Marton,Federico Tombari,Walter Wohlkinger,Christian Potthast,Bernhard Zeisl,Radu Bogdan Rusu,Suat Gedikli,Markus Vincze +8 more
TL;DR: A rapidly growing group of people can acquire 3- D data cheaply and in real time, as these sensors are commodity hardware and sold at low cost.
Proceedings ArticleDOI
CAD-model recognition and 6DOF pose estimation using 3D cues
Aitor Aldoma,Markus Vincze,Nico Blodow,David Gossow,Suat Gedikli,Radu Bogdan Rusu,Gary Bradski +6 more
TL;DR: The Clustered Viewpoint Feature Histogram (CVFH) is described and it is shown that it can be effectively used to recognize objects and 6DOF pose in real environments dealing with partial occlusion, noise and different sensors atributes for training and recognition data.
Journal ArticleDOI
The STRANDS Project: Long-Term Autonomy in Everyday Environments
Nick Hawes,Christopher Burbridge,Ferdian Jovan,Lars Kunze,Bruno Lacerda,Lenka Mudrova,Jay Young,Jeremy L. Wyatt,Denise Hebesberger,Tobias Körtner,Rares Ambrus,Nils Bore,John Folkesson,Patric Jensfelt,Lucas Beyer,Alexander Hermans,Bastian Leibe,Aitor Aldoma,Thomas Faulhammer,Michael Zillich,Markus Vincze,Eris Chinellato,Muhannad Al-Omari,Paul Duckworth,Yiannis Gatsoulis,David C. Hogg,Anthony G. Cohn,Christian Dondrup,Jaime Pulido Fentanes,Tomas Krajnik,Joao Machado Santos,Tom Duckett,Marc Hanheide +32 more
TL;DR: The approach used to enable long-term autonomous operation in everyday environments is described and how the robots are able to use their long run times to improve their own performance is described.
Book ChapterDOI
A global hypotheses verification method for 3d object recognition
TL;DR: Peculiar to this approach is the inherent ability to detect significantly occluded objects without increasing the amount of false positives, so that the operating point of the object recognition algorithm can nicely move toward a higher recall without sacrificing precision.
Book ChapterDOI
OUR-CVFH – Oriented, Unique and Repeatable Clustered Viewpoint Feature Histogram for Object Recognition and 6DOF Pose Estimation
TL;DR: A novel method to estimate a unique and repeatable reference frame in the context of 3D object recognition from a single viewpoint based on global descriptors with substantial improvement is presented regarding accuracy in recognition and 6DOF pose estimation, as well as in terms of computational performance.