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Stream-based Active Learning for Efficient and Adaptive Classification of 3D Objects

Narr, Alexander and Triebel, Rudolph and Cremers, Daniel (2016) Stream-based Active Learning for Efficient and Adaptive Classification of 3D Objects. In: IEEE International Conference on Robotics and Automation ICRA. Int. Conf. on Robotics and Automation, Stockholm, Sweden.

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We present a new Active Learning approach for classifying objects from streams of 3D point cloud data. The major problems here are the non-uniform occurence of class instances and the unbalanced numbers of samples per class. We show that standard online learning methods based on decision trees perform comparably bad for such data streams, which are however particularly relevant for mobile robots that need to learn semantics persistently. To address this, we use Mondrian forests (MF), a recent online learning algorithm that is independent on the data order. We present an extension of that algorithm and show that MF are less overconfident than standard Random Forests. In experiments on the KITTI benchmark, we show that this leads to a substantially improved classification performance for data streams, rendering our approach very attractive for lifelong robot learning applications.

Item URL in elib:https://elib.dlr.de/109255/
Document Type:Conference or Workshop Item (Speech, Poster)
Title:Stream-based Active Learning for Efficient and Adaptive Classification of 3D Objects
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Narr, AlexanderAlexander.Narr (at) dlr.deUNSPECIFIED
Triebel, RudolphRudolph.Triebel (at) dlr.deUNSPECIFIED
Journal or Publication Title:IEEE International Conference on Robotics and Automation ICRA
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Keywords:Active Learning, Mondrian Forests
Event Title:Int. Conf. on Robotics and Automation
Event Location:Stockholm, Sweden
Event Type:international Conference
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Deposited By: Triebel, Rudolph
Deposited On:20 Dec 2016 10:55
Last Modified:31 Jul 2019 20:06

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