Egomotion Estimation by Point-Cloud Back-Mapping
نویسندگان
چکیده
We consider egomotion estimation in the context of driverassistance 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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