Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)

A General Means for Depth Data Error Estimation of Depth Sensors

Authors
Qian She, Hongyang Yu
Corresponding Author
Qian She
Available Online May 2019.
DOI
10.2991/cnci-19.2019.57How to use a DOI?
Keywords
RGB-D sensors, Depth data, point clouds, RMS error.
Abstract

At present the depth error estimation of the RGB-D sensor is aimed at a specific depth camera, the disadvantages of these methods is that it is put forword for a specific sensor and cannot be applied to the other sensors.In order to solve this problem, this paper proposes a general method to estimate the root mean square (RMS) error of depth data provided by general three-dimensional sensors. The method is applicable to three-dimensional sensors based on structured light, time-of-flight, stereo vision and other technologies. Use a common checkerboard to detect corner points and get two point clouds, one is the real point cloud of the image corner, and the other is the estimated point cloud of the corner point given by the device. After registrating of the two point clouds, the RMS error is calculated. The RMS error is generalized as a function of the distance between the RGB-D sensor and the checkerboard .The accuracy and practicability of the proposed method are verified by comparing it with the existing advanced depth error estimation methods.

Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)
Series
Advances in Computer Science Research
Publication Date
May 2019
ISBN
10.2991/cnci-19.2019.57
ISSN
2352-538X
DOI
10.2991/cnci-19.2019.57How to use a DOI?
Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Qian She
AU  - Hongyang Yu
PY  - 2019/05
DA  - 2019/05
TI  - A General Means for Depth Data Error Estimation of Depth Sensors
BT  - Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)
PB  - Atlantis Press
SP  - 418
EP  - 425
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnci-19.2019.57
DO  - 10.2991/cnci-19.2019.57
ID  - She2019/05
ER  -