Multispectral stereo odometry

Tarek Mouats, Nabil Aouf, Angel Domingo Sappa, Cristhian Aguilera, Ricardo Toledo

    Research output: Contribution to journalArticleResearchpeer-review

    45 Citations (Scopus)


    © 2014 IEEE. In this paper, we investigate the problem of visual odometry for ground vehicles based on the simultaneous utilization of multispectral cameras. It encompasses a stereo rig composed of an optical (visible) and thermal sensors. The novelty resides in the localization of the cameras as a stereo setup rather than two monocular cameras of different spectrums. To the best of our knowledge, this is the first time such task is attempted. Log-Gabor wavelets at different orientations and scales are used to extract interest points from both images. These are then described using a combination of frequency and spatial information within the local neighborhood. Matches between the pairs of multimodal images are computed using the cosine similarity function based on the descriptors. Pyramidal Lucas-Kanade tracker is also introduced to tackle temporal feature matching within challenging sequences of the data sets. The vehicle egomotion is computed from the triangulated 3-D points corresponding to the matched features. A windowed version of bundle adjustment incorporating Gauss-Newton optimization is utilized for motion estimation. An outlier removal scheme is also included within the framework to deal with outliers. Multispectral data sets were generated and used as test bed. They correspond to real outdoor scenarios captured using our multimodal setup. Finally, detailed results validating the proposed strategy are illustrated.
    Original languageEnglish
    Article number6911962
    Pages (from-to)1210-1224
    JournalIEEE Transactions on Intelligent Transportation Systems
    Issue number3
    Publication statusPublished - 1 Jun 2015


    • Egomotion estimation
    • feature matching
    • multispectral odometry (MO)
    • optical flow
    • stereo odometry
    • thermal imagery

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