Scopus: 9 cites, Google Scholar: cites
Depth Data Error Modeling of the ZED 3D Vision Sensor from Stereolabs
Ortiz, Luis Enrique (Universidade Federal do Rio Grande do Norte. Department of Computer Engineering and Automation)
Cabrera, Elizabeth V. (Universidade Federal do Rio Grande do Norte. Department of Computer Engineering and Automation)
Goncalves, Luiz M. (Universidade Federal do Rio Grande do Norte. Department of Computer Engineering and Automation)

Data: 2018
Resum: The ZED camera is binocular vision system that can be used to provide a 3D perception of the world. It can be applied in autonomous robot navigation, virtual reality, tracking, motion analysis and so on. This paper proposes a mathematical error model for depth data estimated by the ZED camera with its several resolutions of operation. For doing that, the ZED is attached to a Nvidia Jetson TK1 board providing an embedded system that is used for processing raw data acquired by ZED from a 3D checkerboard. Corners are extracted from the checkerboard using RGB data, and a 3D reconstruction is done for these points using disparity data calculated from the ZED camera, coming up with a partially ordered, and regularly distributed (in 3D space) point cloud of corners with given coordinates, which are computed by the device software. These corners also have their ideal world (3D) positions known with respect to the coordinate frame origin that is empirically set in the pattern. Both given (computed) coordinates from the camera's data and known (ideal) coordinates of a corner can, thus, be compared for estimating the error between the given and ideal point locations of the detected corner cloud. Subsequently, using a curve fitting technique, we obtain the equations that model the RMS (Root Mean Square) error. This procedure is repeated for several resolutions of the ZED sensor, and at several distances. Results showed its best effectiveness with a maximum distance of approximately sixteen meters, in real time, which allows its use in robotic or other online applications.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès.
Document: article ; recerca ; publishedVersion
Matèria: Depth Data ; RMS Error ; Stereo Vision ; Stereolabs ZED
Publicat a: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 17 Núm. 1 (2018) , p. 1-15 (Regular Issue) , ISSN 1577-5097

Adreça original:
DOI: 10.5565/rev/elcvia.1084

15 p, 1.8 MB

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