Stream-based Active Learning for efficient and adaptive classification of 3D objects

Narr A, Triebel R, Cremers D (2016)

Publication Type: Conference contribution

Publication year: 2016

Journal

Proceedings - IEEE International Conference on Robotics and Automation Institute of Electrical and Electronics Engineers

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2016-June

Pages Range: 227-233

Conference Proceedings Title: Proceedings - IEEE International Conference on Robotics and Automation

Event location: Stockholm, SWE

ISBN: 9781467380263

DOI: 10.1109/ICRA.2016.7487138

Abstract

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 occurrence 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.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Narr, A., Triebel, R., & Cremers, D. (2016). Stream-based Active Learning for efficient and adaptive classification of 3D objects. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 227-233). Stockholm, SWE: Institute of Electrical and Electronics Engineers Inc..

MLA:

Narr, Alexander, Rudolph Triebel, and Daniel Cremers. "Stream-based Active Learning for efficient and adaptive classification of 3D objects." Proceedings of the 2016 IEEE International Conference on Robotics and Automation, ICRA 2016, Stockholm, SWE Institute of Electrical and Electronics Engineers Inc., 2016. 227-233.

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