Abstract
We consider egomotion estimation in the context of driver-assistance systems. In order to estimate the actual vehicle movement we only apply stereo cameras (and not any additional sensor). The paper proposes a visual odometry method by back-mapping clouds of reconstructed 3D points. Our method, called stereo-vision point-cloud back mapping method (sPBM), aims at minimizing 3D back-projection errors. We report about extensive experiments for sPBM. At this stage we consider accuracy as being the first priority; optimizing run-time performance will need to be considered later. Accurately estimated motion among subsequent frames of a recorded video sequence can then be used, for example, for 3D roadside reconstruction.
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Geng, H., Nicolescu, R., Klette, R. (2014). Egomotion Estimation by Point-Cloud Back-Mapping. In: Chmielewski, L.J., Kozera, R., Shin, BS., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2014. Lecture Notes in Computer Science, vol 8671. Springer, Cham. https://doi.org/10.1007/978-3-319-11331-9_28
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DOI: https://doi.org/10.1007/978-3-319-11331-9_28
Publisher Name: Springer, Cham
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