Automatic Calibration of an Industrial RGB-D Camera Network Using Retroreflective Fiducial Markers.

ABSTRACT: This paper describes a non-invasive, automatic, and robust method for calibrating a scalable RGB-D sensor network based on retroreflective ArUco markers and the iterative closest point (ICP) scheme. We demonstrate the system by calibrating a sensor network comprised of six sensor nodes positioned in a relatively large industrial robot cell with an approximate size of 10 x 10 x 4 m . Here, the automatic calibration achieved an average Euclidean error of 3 c m at distances up to 9 . 45 m . To achieve robustness, we apply several innovative techniques: Firstly, we mitigate the ambiguity problem that occurs when detecting a marker at long range or low resolution by comparing the camera projection with depth data. Secondly, we use retroreflective fiducial markers in the RGB-D calibration for im

SUBMITTER: Aalerud A 

PROVIDER: S-EPMC6479303 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature