  In this work we propose long wave infrared (LWIR) imagery as a viable
supporting modality for semantic segmentation using learning-based techniques.
We first address the problem of RGB-thermal camera calibration by proposing a
passive calibration target and procedure that is both portable and easy to use.
Second, we present PST900, a dataset of 894 synchronized and calibrated RGB and
Thermal image pairs with per pixel human annotations across four distinct
classes from the DARPA Subterranean Challenge. Lastly, we propose a CNN
architecture for fast semantic segmentation that combines both RGB and Thermal
imagery in a way that leverages RGB imagery independently. We compare our
method against the state-of-the-art and show that our method outperforms them
in our dataset.
