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3D Pose Estimation of Vehicles Using Stereo Camera

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Transportation Technologies for Sustainability

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For advanced driver assistance systems, the 3D poses and motion states of oncoming and intersecting vehicles represent important information. This work describes methods for 3D vehicle pose estimation based on a motion-attributed 3D point cloud generated. First, stereo and optical flow information is computed for 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. Classical ICP optimization is based on the Euclidean distance metric. Its computational efficiency can be significantly increased by applying a quaternion-based optimization scheme. In vehicle-based small-baseline stereo systems, it is favorable to use a polar distance metric which especially takes into account the error distribution of the stereo measurement process. To...

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Abbreviations

3D Pose estimation:

Estimation of the translational and rotational degrees of freedom of an object, usually corresponding to its position in 3D space and its orientation angles. For articulated objects additional, internal degrees of freedom are estimated.

Driver assistance system:

A system which helps the driver of a car in specific traffic situations in order to increase safety.

Iterative closest point (ICP) algorithm:

An algorithm which finds a set of translation and rotation parameters fitting a measured point cloud to a model.

Optical flow:

The optical flow vector denotes the motion in image space of the projection of a scene point between two subsequent images of a sequence.

Quaternion:

Number system which extends the system of complex numbers. In computer vision and computer graphics, quaternions are used for the efficient representation of rotations.

Scene flow:

Vector field describing the 3D scene points visible in the image and their current motion in 3D space. Projecting the scene flow into the image plane yields the optical flow.

Stereo vision:

3D reconstruction of a scene based on a pair of images.

Yaw angle:

Angle measuring the rotation of a vehicle around its vertical axis.

Yaw rate:

Temporal derivative of the yaw angle.

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Author information

Authors and Affiliations

  1. EvoBus GmbH, Carl-Zeiss-Straße 2, 89231, Neu-Ulm, Germany

    Dr. Björn Barrois

  2. Image Analysis Group, Dortmund University of Technology, Otto-Hahn-Str. 4, 44227, Dortmund, Germany

    Christian Wöhler

Authors
  1. Dr. Björn Barrois
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  2. Christian Wöhler
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Corresponding author

Correspondence to Björn Barrois .

Editor information

Editors and Affiliations

  1. Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX, USA

    Mehrdad Ehsani

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Fei-Yue Wang

  3. Center for Urban Transportation Research, University of South Florida, Tampa, FL, USA

    Gary L. Brosch

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Barrois, B., Wöhler, C. (2013). 3D Pose Estimation of Vehicles Using Stereo Camera. In: Ehsani, M., Wang, FY., Brosch, G.L. (eds) Transportation Technologies for Sustainability. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5844-9_484

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