Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/63808

Development of a Highly Precise Place Recognition Module for Effective Human-robot Interactions in Changing Lighting and Viewpoint Conditions

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Title:Development of a Highly Precise Place Recognition Module for Effective Human-robot Interactions in Changing Lighting and Viewpoint Conditions
Authors:Baumgartl, Hermann
Buettner, Ricardo
Keywords:Human-Robot Interactions
convolutional neural networks
human-robot interaction
inductive bias transfer learning
machine learning
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Date Issued:07 Jan 2020
Abstract:We present a highly precise and robust module for indoor place recognition, extending the work by Lemaignan et al. and Robert Jr. by giving the robot the ability to recognize its environment context. We developed a full end-to-end convolutional neural network architecture, using a pre-trained deep convolutional neural network and the explicit inductive bias transfer learning strategy. Experimental results based on the York University and Rzeszów University dataset show excellent performance values (over 94.75 and 97.95 percent accuracy) and a high level of robustness over changes in camera viewpoint and lighting conditions, outperforming current benchmarks. Furthermore, our architecture is 82.46 percent smaller than the current benchmark, making our module suitable for embedding into mobile robots and easily adoptable to other datasets without the need for heavy adjustments.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63808
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.069
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Human-Robot Interactions


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