Clemens RabeAbstractDriver assistance systems of the future require a thorough understanding
of the car's environment. In this thesis, a novel principle (6D-Vision)
is presented and investigated in detail, which allows the
reconstruction of the 3D motion field from the image sequence obtained
by a stereo camera system. Given correspondences of stereo measurements
over time, this principle estimates the 3D position and the 3D motion
vector of selected points using Kalman Filters, resulting in a real-time
estimation of the observed motion field. To estimate the absolute
motion field, the ego-motion of the moving observer must be known
precisely. Thus, a novel algorithm to estimate the ego-motion from the
image sequence is presented. As the 6D-Vision principle is not
restricted to particular image processing algorithms, various optical
flow and stereo algorithms are evaluated. In addition, two novel scene
flow algorithms are introduced, measuring the optical flow and stereo
information in a combined approach. This yields more precise and robust
results. The application to real-world data, including a demonstrator
vehicle for autonomous collision avoidance, is illustrated throughout
the thesis.
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