@InProceedings{8081319,
  author = {F. Bonardi and S. Ainouz and R. Boutteau and Y. Dupuis and X. Savatier and P. Vasseur},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {A novel global image description approach for long term vehicle localization},
  year = {2017},
  pages = {808-812},
  abstract = {Long-term place recognition for vehicles or robots in outdoor environment is still a tackling issue: numerous changes occur in appearance due to illumination variations or weather phenomena for instance, when using visual sensors. Few methods from the literature try to manage different visual sources while it could favor data interoperability across variable sensors. In this paper, we emphasis our works on cases where there is a need to associate data from different imaging sources (optics, sensors size and even spectral ranges). We developed a method with a first camera which composes the visual memory. Afterwards, we consider another camera which partially covers the same journey. Our goal is to associate live images to the prior visual memory thanks to visual features invariant to sensors changes, with the help of a probabilistic approach for the implementation part.},
  keywords = {image recognition;image sensors;object recognition;object tracking;robot vision;global image description approach;visual memory;imaging sources;visual features invariant;data interoperability;visual sensors;long-term place recognition;long term vehicle localization;Visualization;Cameras;Image sensors;Sensor phenomena and characterization;Robustness;Lighting},
  doi = {10.23919/EUSIPCO.2017.8081319},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570342020.pdf},
}

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