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

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

Blog Article

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 ultrastar dc hc570 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   m × 10   m × 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, kotha model dress 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 improved accuracy and detectability.Finally, the repeating ICP refinement uses an exact region of interest such that we employ the precise depth measurements of the retroreflective surfaces only.The complete calibration software and a recorded dataset are publically available and open source.