Version 1
: Received: 22 May 2017 / Approved: 23 May 2017 / Online: 23 May 2017 (09:20:27 CEST)
How to cite:
Fernandez, L.; Avila, V.; Goncalves, L. A Generic Approach for Error Estimation of Depth Data from (Stereo and RGB-D) 3D Sensors. Preprints2017, 2017050170. https://doi.org/10.20944/preprints201705.0170.v1
Fernandez, L.; Avila, V.; Goncalves, L. A Generic Approach for Error Estimation of Depth Data from (Stereo and RGB-D) 3D Sensors. Preprints 2017, 2017050170. https://doi.org/10.20944/preprints201705.0170.v1
Fernandez, L.; Avila, V.; Goncalves, L. A Generic Approach for Error Estimation of Depth Data from (Stereo and RGB-D) 3D Sensors. Preprints2017, 2017050170. https://doi.org/10.20944/preprints201705.0170.v1
APA Style
Fernandez, L., Avila, V., & Goncalves, L. (2017). A Generic Approach for Error Estimation of Depth Data from (Stereo and RGB-D) 3D Sensors. Preprints. https://doi.org/10.20944/preprints201705.0170.v1
Chicago/Turabian Style
Fernandez, L., Viviana Avila and Luiz Goncalves. 2017 "A Generic Approach for Error Estimation of Depth Data from (Stereo and RGB-D) 3D Sensors" Preprints. https://doi.org/10.20944/preprints201705.0170.v1
Abstract
We propose an approach for estimating the error in depth data provided by generic 3D sensors, which are modern devices capable of generating an image (RGB data) and a depth map (distance) or other similar 2.5D structure (e.g. stereo disparity) of the scene. Our approach starts capturing images of a checkerboard pattern devised for the method. Then proceed with the construction of a dense depth map using functions that generally comes with the device SDK (based on disparity or depth). The 2D processing of RGB data is performed next to find the checkerboard corners. Clouds of corner points are finally created (in 3D), over which an RMS error estimation is computed. We come up with a multi-platform system and its verification and evaluation has been done, using the development kit of the board nVIDIA Jetson TK1 with the MS Kinects v1/v2 and the Stereolabs ZED camera. So the main contribution is the error determination procedure that does not need any data set or benchmark, thus relying only on data acquired on-the-fly. With a simple checkerboard, our approach is able to determine the error for any device. Envisioned application is on 3D reconstruction for robotic vision, with a series of 3D vision sensors embarked in robots (UAV of type quadcopter and terrestrial robots) for high-precision map construction, which can be used for sensing and monitoring.
Keywords
accuracy; depth data; RMS error; 3D vision sensors; stereo disparity
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
