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3D pose estimation of vehicles using a stereo camera

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5 Author(s)
Barrois, B. ; Group Res., Environ. Perception, Daimler AG, Ulm, Germany ; Hristova, S. ; Wöhler, C. ; Kummert, F.
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This study introduces an approach to three-dimensional vehicle pose estimation using a stereo camera system. After computation of stereo and optical flow on the investigated scene, a four-dimensional clustering approach separates the static from the moving objects in the scene. The iterative closest point algorithm (ICP) estimates the vehicle pose using a cuboid as a weak vehicle model. In contrast to classical ICP optimisation a polar distance metric is used which especially takes into account the error distribution of the stereo measurement process. The tracking approach is based on tracking-by-detection such that no temporal filtering is used. The method is evaluated on seven different real-world sequences, where different stereo algorithms, baseline distances, distance metrics, and optimisation algorithms are examined. The results show that the proposed polar distance metric yields a higher accuracy for yaw angle estimation of vehicles than the common Euclidean distance metric, especially when using pixel-accurate stereo points.

Published in:

Intelligent Vehicles Symposium, 2009 IEEE

Date of Conference:

3-5 June 2009

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