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

Simple item page
dc.contributor.author Baumgartl, Hermann
dc.contributor.author Buettner, Ricardo
dc.date.accessioned 2020-01-04T07:15:07Z
dc.date.available 2020-01-04T07:15:07Z
dc.date.issued 2020-01-07
dc.description.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.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.069
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/63808
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Human-Robot Interactions
dc.subject convolutional neural networks
dc.subject human-robot interaction
dc.subject inductive bias transfer learning
dc.subject machine learning
dc.subject place recognition module
dc.title Development of a Highly Precise Place Recognition Module for Effective Human-robot Interactions in Changing Lighting and Viewpoint Conditions
dc.type Conference Paper
dc.type.dcmi Text
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
0056.pdf
Size:
2.68 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Human-Robot Interactions