Next Article in Journal
Active Disaster Response System for a Smart Building
Previous Article in Journal
Gas Sensors Based on Semiconducting Nanowire Field-Effect Transistors
Article Menu

Export Article

Open AccessArticle
Sensors 2014, 14(9), 17430-17450; doi:10.3390/s140917430

Statistical Analysis-Based Error Models for the Microsoft KinectTM Depth Sensor

1
Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA
2
Barron Associates, Charlottesville, VA 22901, USA
*
Author to whom correspondence should be addressed.
Received: 21 May 2014 / Revised: 9 September 2014 / Accepted: 10 September 2014 / Published: 18 September 2014
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [3060 KB, uploaded 18 September 2014]   |  

Abstract

The stochastic error characteristics of the Kinect sensing device are presented for each axis direction. Depth (z) directional error is measured using a flat surface, and horizontal (x) and vertical (y) errors are measured using a novel 3D checkerboard. Results show that the stochastic nature of the Kinect measurement error is affected mostly by the depth at which the object being sensed is located, though radial factors must be considered, as well. Measurement and statistics-based models are presented for the stochastic error in each axis direction, which are based on the location and depth value of empirical data measured for each pixel across the entire field of view. The resulting models are compared against existing Kinect error models, and through these comparisons, the proposed model is shown to be a more sophisticated and precise characterization of the Kinect error distributions. View Full-Text
Keywords: KinectTM; noise model; statistical noise analysis; calibration KinectTM; noise model; statistical noise analysis; calibration
Figures

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Choo, B.; Landau, M.; DeVore, M.; Beling, P.A. Statistical Analysis-Based Error Models for the Microsoft KinectTM Depth Sensor. Sensors 2014, 14, 17430-17450.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top