Analysis of Illumination Robustness in Long-Term Object Learning

Ingo Keller; Katrin Solveig Lohan

doi:10.1109/ROMAN.2016.7745137

Analysis of Illumination Robustness in Long-Term Object Learning

Ingo Keller, Katrin Solveig Lohan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

112 Downloads (Pure)

Abstract

In this article we evaluate the incremental object learning approach of the iCub humanoid robot which is directed towards long-term engagement. Affordable robot companion systems are currently entering the consumer market which highlights the importance in understanding environmental influences on robotic systems under real world conditions. If a robot is to be sent into the real world or different robots/sensors are to be used, we need our algorithms to be independent from both illumination and sensor influenced changes. In our work, we investigate the robustness of the interactive object learning to linear and non-linear lighting changes which can occur due to illumination changes throughout the day or the sensors used. Our results with the models we use suggest that the current method is susceptible to these changes. Therefore, we provide an adjustment to the current method to be able to cope with this problem.

Original language	English
Title of host publication	2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)
Publisher	IEEE
ISBN (Electronic)	9781509039296
DOIs	https://doi.org/10.1109/ROMAN.2016.7745137
Publication status	Published - 17 Nov 2016
Event	25th IEEE International Symposium on Robot and Human Interactive Communication 2016 - New York, United States Duration: 26 Aug 2016 → 31 Aug 2016

Publication series

Name	IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)
ISSN (Electronic)	1944-9437

Conference

Conference	25th IEEE International Symposium on Robot and Human Interactive Communication 2016
Abbreviated title	RO-MAN 2016
Country/Territory	United States
City	New York
Period	26/08/16 → 31/08/16

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10.1109/ROMAN.2016.7745137

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@inproceedings{cc8b3b67dc764c1da33ed2614cf16fb7,

title = "Analysis of Illumination Robustness in Long-Term Object Learning",

abstract = "In this article we evaluate the incremental object learning approach of the iCub humanoid robot which is directed towards long-term engagement. Affordable robot companion systems are currently entering the consumer market which highlights the importance in understanding environmental influences on robotic systems under real world conditions. If a robot is to be sent into the real world or different robots/sensors are to be used, we need our algorithms to be independent from both illumination and sensor influenced changes. In our work, we investigate the robustness of the interactive object learning to linear and non-linear lighting changes which can occur due to illumination changes throughout the day or the sensors used. Our results with the models we use suggest that the current method is susceptible to these changes. Therefore, we provide an adjustment to the current method to be able to cope with this problem.",

author = "Ingo Keller and Lohan, \{Katrin Solveig\}",

year = "2016",

month = nov,

day = "17",

doi = "10.1109/ROMAN.2016.7745137",

language = "English",

series = "IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)",

publisher = "IEEE",

booktitle = "2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)",

address = "United States",

note = "25th IEEE International Symposium on Robot and Human Interactive Communication 2016, RO-MAN 2016 ; Conference date: 26-08-2016 Through 31-08-2016",

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Keller, I & Lohan, KS 2016, Analysis of Illumination Robustness in Long-Term Object Learning. in 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)., 7745137, IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), IEEE, 25th IEEE International Symposium on Robot and Human Interactive Communication 2016, New York, United States, 26/08/16. https://doi.org/10.1109/ROMAN.2016.7745137

Analysis of Illumination Robustness in Long-Term Object Learning. / Keller, Ingo; Lohan, Katrin Solveig.
2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, 2016. 7745137 (IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Analysis of Illumination Robustness in Long-Term Object Learning

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AU - Lohan, Katrin Solveig

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N2 - In this article we evaluate the incremental object learning approach of the iCub humanoid robot which is directed towards long-term engagement. Affordable robot companion systems are currently entering the consumer market which highlights the importance in understanding environmental influences on robotic systems under real world conditions. If a robot is to be sent into the real world or different robots/sensors are to be used, we need our algorithms to be independent from both illumination and sensor influenced changes. In our work, we investigate the robustness of the interactive object learning to linear and non-linear lighting changes which can occur due to illumination changes throughout the day or the sensors used. Our results with the models we use suggest that the current method is susceptible to these changes. Therefore, we provide an adjustment to the current method to be able to cope with this problem.

AB - In this article we evaluate the incremental object learning approach of the iCub humanoid robot which is directed towards long-term engagement. Affordable robot companion systems are currently entering the consumer market which highlights the importance in understanding environmental influences on robotic systems under real world conditions. If a robot is to be sent into the real world or different robots/sensors are to be used, we need our algorithms to be independent from both illumination and sensor influenced changes. In our work, we investigate the robustness of the interactive object learning to linear and non-linear lighting changes which can occur due to illumination changes throughout the day or the sensors used. Our results with the models we use suggest that the current method is susceptible to these changes. Therefore, we provide an adjustment to the current method to be able to cope with this problem.

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M3 - Conference contribution

T3 - IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

BT - 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

PB - IEEE

T2 - 25th IEEE International Symposium on Robot and Human Interactive Communication 2016

Y2 - 26 August 2016 through 31 August 2016

ER -

Analysis of Illumination Robustness in Long-Term Object Learning

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