A common prerequisite for many vision-based driver assistance systems is the knowledge of the vehicle's own movement. In this paper we propose a novel approach for estimating the egomotion of the vehicle from a sequence of stereo images. Our method is directly based on the trifocal geometry between image triples, thus no time expensive recovery of the 3-dimensional scene structure is needed. The only assumption we make is a known camera geometry, where the calibration may also vary over time. We employ an Iterated Sigma Point Kalman Filter in combination with a RANSAC-based outlier rejection scheme which yields robust frame-to-frame motion estimation even in dynamic environments. A high-accuracy inertial navigation system is used to evaluate our results on challenging real-world video sequences. Experiments show that our approach is clearly superior compared to other filtering techniques in terms of both, accuracy and run-time.


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    Title :

    Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme


    Contributors:
    Kitt, B (author) / Geiger, A (author) / Lategahn, H (author)


    Publication date :

    2010-06-01


    Size :

    1481929 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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