Olga Vysotska

PhD Student
Contact:
Email: olga.vysotska@nulluni-bonn.de
Tel: +49 - 228 - 73 - 29 06
Fax: +49 - 228 - 73 - 27 12
Office: Nussallee 15, 1. OG, room 1.006
Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn
Google Scholar Profile

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Research Interests

  • Visual Place Recognition
  • Localization, Mapping using Publicly Available Data

Short CV

Olga Vysotska is a PhD Student for the photogrammetry at the University of Bonn since August 2014. She received her Master Degree in Applied Computer Science at the University of Freiburg, Germany in 2014. Her Master thesis was about “Automatic Channel Selection and Neural Signal Estimation across Channels of Neural Probes”, which she wrote at Autonomous Intelligent Systems group of Prof. Dr. Wolfram Burgard. Before that, in 2011 she finished her Bachelor studies in Applied Mathematics, Faculty of Cybernetics, T.Shevchenko University of Kyiv, Ukraine.

Code releases

Teaching

  • Master Project Mobile Mapping Systems, ss 2017, ss 2016, ss 2015
  • Exercises for Photogrammetry & Remote Sensing, ws 2016/17, ws 2015/16, ws 2014/15

Publications

2017

  • O. Vysotska and C. Stachniss, “Improving SLAM by Exploiting Building Information from Publicly Available Maps and Localization Priors,” PFG — Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol. 85, iss. 1, pp. 53-65, 2017.
    [BibTeX] [PDF]
    @Article{vysotska17pfg,
    Title = {Improving SLAM by Exploiting Building Information from Publicly Available Maps and Localization Priors},
    Author = {Vysotska, O. and Stachniss, C.},
    Journal = {PFG -- Journal of Photogrammetry, Remote Sensing and Geoinformation Science},
    Year = {2017},
    Number = {1},
    Pages = {53-65},
    Volume = {85},
    Url = {http://link.springer.com/article/10.1007/s41064-017-0006-3}
    }

2016

  • O. Vysotska and C. Stachniss, “Exploiting Building Information from Publicly Available Maps in Graph-Based SLAM,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2016.
    [BibTeX] [PDF]
    [none]
    @InProceedings{vysotska16iros,
    Title = {Exploiting Building Information from Publicly Available Maps in Graph-Based SLAM},
    Author = {O. Vysotska and C. Stachniss},
    Booktitle = iros,
    Year = {2016},
    Abstract = {[none]},
    Url = {http://www.ipb.uni-bonn.de/pdfs/vysotska16iros.pdf}
    }

  • O. Vysotska and C. Stachniss, “Lazy Data Association For Image Sequences Matching Under Substantial Appearance Changes,” IEEE Robotics and Automation Letters (RA-L)and IEEE International Conference on Robotics & Automation (ICRA), vol. 1, iss. 1, pp. 1-8, 2016. doi:10.1109/LRA.2015.2512936
    [BibTeX] [PDF] [Code] [Video]
    Localization is an essential capability for mobile robots and the ability to localize in changing environments is key to robust outdoor navigation. Robots operating over extended periods of time should be able to handle substantial appearance changes such as those occurring over seasons or under different weather conditions. In this letter, we investigate the problem of efficiently coping with seasonal appearance changes in online localization. We propose a lazy data association approach for matching streams of incoming images to a reference image sequence in an online fashion. We present a search heuristic to quickly find matches between the current image sequence and a database using a data association graph. Our experiments conducted under substantial seasonal changes suggest that our approach can efficiently match image sequences while requiring a comparably small number of image to image comparisons

    @Article{vysotska16ral,
    Title = {Lazy Data Association For Image Sequences Matching Under Substantial Appearance Changes},
    Author = {O. Vysotska and C. Stachniss},
    Journal = {IEEE Robotics and Automation Letters (RA-L)and IEEE International Conference on Robotics \& Automation (ICRA)},
    Year = {2016},
    Number = {1},
    Pages = {1-8},
    Volume = {1},
    Abstract = {Localization is an essential capability for mobile robots and the ability to localize in changing environments is key to robust outdoor navigation. Robots operating over extended periods of time should be able to handle substantial appearance changes such as those occurring over seasons or under different weather conditions. In this letter, we investigate the problem of efficiently coping with seasonal appearance changes in online localization. We propose a lazy data association approach for matching streams of incoming images to a reference image sequence in an online fashion. We present a search heuristic to quickly find matches between the current image sequence and a database using a data association graph. Our experiments conducted under substantial seasonal changes suggest that our approach can efficiently match image sequences while requiring a comparably small number of image to image comparisons},
    Doi = {10.1109/LRA.2015.2512936},
    Timestamp = {2016.04.18},
    Url = {http://www.ipb.uni-bonn.de/pdfs/vysotska16ral-icra.pdf},
    CodeUrl = {https://github.com/Photogrammetry-Robotics-Bonn/online_place_recognition},
    VideoUrl = {https://www.youtube.com/watch?v=l-hNk7Z4lSk},
    }

2015

  • O. Vysotska, T. Naseer, L. Spinello, W. Burgard, and C. Stachniss, “Efficient and Effective Matching of Image Sequences Under Substantial Appearance Changes Exploiting GPS Prior,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , 2015, pp. 2774-2779. doi:10.1109/ICRA.2015.7139576
    [BibTeX] [PDF]
    The ability to localize a robot is an important capability and matching of observations under substantial changes is a prerequisite for robust long-term operation. This paper investigates the problem of efficiently coping with seasonal changes in image data. We present an extension of a recent approach [15] to visual image matching using sequence information. Our extension allows for exploiting GPS priors in the matching process to overcome the main computational bottleneck of the previous method and to handle loops within the image sequences. We present an experimental evaluation using real world data containing substantial seasonal changes and show that our approach outperforms the previous method in case a noisy GPS pose prior is available.

    @InProceedings{vysotska15icra,
    Title = {Efficient and Effective Matching of Image Sequences Under Substantial Appearance Changes Exploiting GPS Prior},
    Author = {O. Vysotska and T. Naseer and L. Spinello and W. Burgard and C. Stachniss},
    Booktitle = ICRA,
    Year = {2015},
    Pages = {2774-2779},
    Abstract = {The ability to localize a robot is an important capability and matching of observations under substantial changes is a prerequisite for robust long-term operation. This paper investigates the problem of efficiently coping with seasonal changes in image data. We present an extension of a recent approach [15] to visual image matching using sequence information. Our extension allows for exploiting GPS priors in the matching process to overcome the main computational bottleneck of the previous method and to handle loops within the image sequences. We present an experimental evaluation using real world data containing substantial seasonal changes and show that our approach outperforms the previous method in case a noisy GPS pose prior is available.},
    Doi = {10.1109/ICRA.2015.7139576},
    Timestamp = {2015.06.29},
    Url = {http://www.ipb.uni-bonn.de/pdfs/vysotska15icra.pdf}
    }

  • O. Vysotska and C. Stachniss, “Lazy Sequences Matching Under Substantial Appearance Changes,” in Workshop on Visual Place Recognition in Changing Environments at the IEEE Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA) , 2015.
    [BibTeX] [PDF]
    [none]
    @InProceedings{vysotska15icraws,
    Title = {Lazy Sequences Matching Under Substantial Appearance Changes},
    Author = {O. Vysotska and C. Stachniss},
    Booktitle = {Workshop on Visual Place Recognition in Changing Environments at the IEEE } # ICRA,
    Year = {2015},
    Abstract = {[none]},
    Timestamp = {2015.06.29},
    Url = {http://www.ipb.uni-bonn.de/pdfs/vysotska15icra-ws.pdf}
    }

2014

  • O. Vysotska, B. Frank, I. Ulbert, O. Paul, P. Ruther, C. Stachniss, and W. Burgard, “Automatic Channel Selection and Neural Signal Estimation across Channels of Neural Probes,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , Chicago, USA, 2014.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Vysotska2014,
    Title = {Automatic Channel Selection and Neural Signal Estimation across Channels of Neural Probes},
    Author = {O. Vysotska and B. Frank and I. Ulbert and O. Paul and P. Ruther and C. Stachniss and W. Burgard},
    Booktitle = iros,
    Year = {2014},
    Address = {Chicago, USA},
    Abstract = {[none]},
    Timestamp = {2014.09.22},
    Url = {http://www2.informatik.uni-freiburg.de/~stachnis/pdf/vysotska14iros.pdf}
    }

2013

  • I. Bogoslavskyi, O. Vysotska, J. Serafin, G. Grisetti, and C. Stachniss, “Efficient Traversability Analysis for Mobile Robots using the Kinect Sensor,” in Proceedings of the European Conference on Mobile Robots (ECMR) , Barcelona, Spain, 2013.
    [BibTeX] [PDF]
    [none]
    @InProceedings{Bogoslavskyi2013,
    Title = {Efficient Traversability Analysis for Mobile Robots using the Kinect Sensor},
    Author = {I. Bogoslavskyi and O. Vysotska and J. Serafin and G. Grisetti and C. Stachniss},
    Booktitle = ECMR,
    Year = {2013},
    Address = {Barcelona, Spain},
    Abstract = {[none]},
    Timestamp = {2014.04.24},
    Url = {http://www.informatik.uni-freiburg.de/~stachnis/pdf/bogoslavskyi13ecmr.pdf}
    }

igg
Institute of Geodesy
and Geoinformation
lwf
Faculty of Agriculture
ubn
University of Bonn