  The combination of range sensors with color cameras can be very useful for
robot navigation, semantic perception, manipulation, and telepresence. Several
methods of combining range- and color-data have been investigated and
successfully used in various robotic applications. Most of these systems suffer
from the problems of noise in the range-data and resolution mismatch between
the range sensor and the color cameras, since the resolution of current range
sensors is much less than the resolution of color cameras. High-resolution
depth maps can be obtained using stereo matching, but this often fails to
construct accurate depth maps of weakly/repetitively textured scenes, or if the
scene exhibits complex self-occlusions. Range sensors provide coarse depth
information regardless of presence/absence of texture. The use of a calibrated
system, composed of a time-of-flight (TOF) camera and of a stereoscopic camera
pair, allows data fusion thus overcoming the weaknesses of both individual
sensors. We propose a novel TOF-stereo fusion method based on an efficient
seed-growing algorithm which uses the TOF data projected onto the stereo image
pair as an initial set of correspondences. These initial "seeds" are then
propagated based on a Bayesian model which combines an image similarity score
with rough depth priors computed from the low-resolution range data. The
overall result is a dense and accurate depth map at the resolution of the color
cameras at hand. We show that the proposed algorithm outperforms 2D image-based
stereo algorithms and that the results are of higher resolution than
off-the-shelf color-range sensors, e.g., Kinect. Moreover, the algorithm
potentially exhibits real-time performance on a single CPU.
