  In this paper, we propose a thermal-infrared simultaneous localization and
mapping (SLAM) system enhanced by sparse depth measurements from Light
Detection and Ranging (LiDAR). Thermal-infrared cameras are relatively robust
against fog, smoke, and dynamic lighting conditions compared to RGB cameras
operating under the visible spectrum. Due to the advantages of thermal-infrared
cameras, exploiting them for motion estimation and mapping is highly appealing.
However, operating a thermal-infrared camera directly in existing vision-based
methods is difficult because of the modality difference. This paper proposes a
method to use sparse depth measurement for 6-DOF motion estimation by directly
tracking under 14- bit raw measurement of the thermal camera. In addition, we
perform a refinement to improve the local accuracy and include a loop closure
to maintain global consistency. The experimental results demonstrate that the
system is not only robust under various lighting conditions such as day and
night, but also overcomes the scale problem of monocular cameras. The video is
available at https://youtu.be/oO7lT3uAzLc.
