Online Depth Calibration for RGB-D Cameras using Visual SLAM

Video attachment for paper
Jan Quenzel, Radu Alexandru Rosu, Sebastian Houben, and Sven Behnke:
"Online Depth Calibration for RGB-D Cameras using Visual SLAM"
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, September 2017.
www.ais.uni-bon...
Modern consumer RGB-D cameras are affordable
and provide dense depth estimates at high frame rates. Hence,
they are popular for building dense environment representations.
Yet, the sensors often do not provide accurate depth
estimates since the factory calibration exhibits a static deformation.
We present a novel approach to online depth calibration
that uses a visual SLAM system as reference for the measured
depth. A sparse map is generated and the visual information
is used to correct the static deformation of the measured depth
while missing data is extrapolated using a small number of thin
plate splines (TPS). The corrected depth can then be used to
improve the accuracy of the sparse RGB-D map and the 3D
environment reconstruction. As more data becomes available,
the depth calibration is updated on the fly. Our method does
not rely on a planar geometry like walls or a one-to-one-pixel
correspondence between color and depth camera. Our approach
is evaluated in real-world scenarios and against ground truth
data. Comparison against two popular self-calibration methods
is performed. Furthermore, we show clear visual improvement
on aggregated point clouds with our method.

[@OneisneO]

Fantastic results!