Home/Motion Compensation for Structured Light Sensors

Motion Compensation for Structured Light Sensors

Debjani Biswas and Christoph Mertz
Conference Paper, Carnegie Mellon University, SPIE - Defense, Security and Sensing, April, 2015

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

In order for structured light methods to work outside, the strong background from the sun needs to be suppressed. This can be done with bandpass filters, fast shutters, and background subtraction. In general this last method necessitates the sensor system to be stationary during data taking. The contribution of this paper is a method to compensate for the motion if the system is moving. The key idea is to use video stabilization techniques that work even if the illuminator is switched on and off from one frame to another. We used OpenCV functions and modules to implement a robust and efficient method. We evaluated it under various conditions and tested it on a moving robot outdoors. We will demonstrate that one can not only do 3D reconstruction under strong ambient light, but that it is also possible to observe optical properties of the objects in the environment.

BibTeX Reference
@conference{Biswas-2015-5936,
title = {Motion Compensation for Structured Light Sensors},
author = {Debjani Biswas and Christoph Mertz},
booktitle = {SPIE - Defense, Security and Sensing},
sponsor = {Robotics Consortium sponsored by the US Army Research Laboratory (ARL)},
grantID = {Collaborative Technology Alliance Program, Cooperative Agreement W911NF-10-2-0016},
school = {Robotics Institute , Carnegie Mellon University},
month = {April},
year = {2015},
address = {Pittsburgh, PA},
}
2017-09-13T10:38:44+00:00