/Extrinsic Calibration of 3D Sensors Using a Spherical Target

Extrinsic Calibration of 3D Sensors Using a Spherical Target

Minghao Ruan and Daniel Huber
Conference Paper, Proceedings of the International Conference on 3D Vision, December, 2014

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

With the emergence of relatively low-cost real-time 3D imaging sensors, new applications for suites of 3D sensors are becoming practical. For example, 3D sensors in an industrial robotic workcell can monitor workers’ positions to ensure their safety. This paper introduces a simple-to-use method for extrinsic calibration of multiple 3D sensors observing a common workspace. Traditional planar target camera calibration techniques are not well-suited for such situations, because multiple cameras may not observe the same target. Our method uses a hand-held spherical target, which is imaged from various points within the workspace. The algorithm automatically detects the sphere in a sequence of views and simultaneously estimates the sphere centers and extrinsic parameters to align an arbitrary network of 3D sensors. We demonstrate the approach with examples of calibrating heterogeneous collections of 3D cameras and achieve better results than traditional, image-based calibration.

BibTeX Reference
@conference{Ruan-2014-7957,
author = {Minghao Ruan and Daniel Huber},
title = {Extrinsic Calibration of 3D Sensors Using a Spherical Target},
booktitle = {Proceedings of the International Conference on 3D Vision},
year = {2014},
month = {December},
keywords = {computer vision, calibration, flash lidar, Kinect, stereo},
}
2017-09-13T10:38:50+00:00