  In the context of a localization and tracking application, we developed a
stereo vision system based on cheap low-resolution 80x60 pixels thermal
cameras. We proposed a threefold sub-pixel stereo matching framework (called ST
for Subpixel Thermal): 1) robust features extraction method based on phase
congruency, 2) rough matching of these features in pixel precision, and 3)
refined matching in sub-pixel accuracy based on local phase coherence. We
performed experiments on our very low-resolution thermal images (acquired using
a stereo system we manufactured) as for high-resolution images from a benchmark
dataset. Even if phase congruency computation time is high, it was able to
extract two times more features than state-of-the-art methods such as ORB or
SURF. We proposed a modified version of the phase correlation applied in the
phase congruency feature space for sub-pixel matching. Using simulated stereo,
we investigated how the phase congruency threshold and the sub-image size of
sub-pixel matching can influence the accuracy. We then proved that given our
stereo setup and the resolution of our images, being wrong of 1 pixel leads to
a 500 mm error in the Z position of the point. Finally, we showed that our
method could extract four times more matches than a baseline method ORB +
OpenCV KNN matching on low-resolution images. Moreover, our matches were more
robust. More precisely, when projecting points of a standing person, ST got a
standard deviation of 300 mm when ORB + OpenCV KNN gave more than 1000 mm.
