  Simultaneous Localization and Mapping (SLAM) system typically employ
vision-based sensors to observe the surrounding environment. However, the
performance of such systems highly depends on the ambient illumination
conditions. In scenarios with adverse visibility or in the presence of airborne
particulates (e.g. smoke, dust, etc.), alternative modalities such as those
based on thermal imaging and inertial sensors are more promising. In this
paper, we propose the first complete thermal-inertial SLAM system which
combines neural abstraction in the SLAM front end with robust pose graph
optimization in the SLAM back end. We model the sensor abstraction in the front
end by employing probabilistic deep learning parameterized by Mixture Density
Networks (MDN). Our key strategies to successfully model this encoding from
thermal imagery are the usage of normalized 14-bit radiometric data, the
incorporation of hallucinated visual (RGB) features, and the inclusion of
feature selection to estimate the MDN parameters. To enable a full SLAM system,
we also design an efficient global image descriptor which is able to detect
loop closures from thermal embedding vectors. We performed extensive
experiments and analysis using three datasets, namely self-collected ground
robot and handheld data taken in indoor environment, and one public dataset
(SubT-tunnel) collected in underground tunnel. Finally, we demonstrate that an
accurate thermal-inertial SLAM system can be realized in conditions of both
benign and adverse visibility.
