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Predicting Out-of-View Feature Points for Model-Based Camera Pose Estimation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)
Subtitle of host publicationProceedings of a meeting held 1-5 October 2018, Madrid, Spain
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Pages82-88
Number of pages7
ISBN (Electronic)9781538680940
ISBN (Print)9781538680933
DOIs
DateAccepted/In press - 29 Jun 2018
DateE-pub ahead of print - 7 Jan 2019
DatePublished (current) - Mar 2019

Publication series

Name
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Abstract

In this work we present a novel framework that uses deep learning to predict object feature points that are out- of-view in the input image. This system was developed with the application of model-based tracking in mind, particularly in the case of autonomous inspection robots, where only partial views of the object are available. Out-of-view prediction is enabled by applying scaling to the feature point labels during network training. This is combined with a recurrent neural network architecture designed to provide the final prediction layers with rich feature information from across the spatial extent of the input image. To show the versatility of these out- of-view predictions, we describe how to integrate them in both a particle filter tracker and an optimisation based tracker. To evaluate our work we compared our framework with one that predicts only points inside the image. We show that as the amount of the object in view decreases, being able to predict outside the image bounds adds robustness to the final pose estimation.

    Research areas

  • model based 3-D tracking, partial views, deep learning, computer vision, robotics

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at https://ieeexplore.ieee.org/document/8594297 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 1 MB, PDF-document

DOI

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