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

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 bet...

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Bibliographic Details
Published in2010 IEEE Intelligent Vehicles Symposium pp. 486 - 492
Main Authors Kitt, Bernd, Geiger, Andreas, Lategahn, Henning
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Calibration
Cameras
Geometry
Image sequences
Inertial navigation
Layout
Motion estimation
Robustness
Vehicle dynamics
Video sequences
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Summary: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.
ISBN:1424478669
9781424478668
ISSN:1931-0587
2642-7214
DOI:10.1109/IVS.2010.5548123