  With an ever-widening domain of aerial robotic applications, including many
mission critical tasks such as disaster response operations, search and rescue
missions and infrastructure inspections taking place in GPS-denied
environments, the need for reliable autonomous operation of aerial robots has
become crucial. Operating in GPS-denied areas aerial robots rely on a multitude
of sensors to localize and navigate. Visible spectrum cameras are the most
commonly used sensors due to their low cost and weight. However, in
environments that are visually-degraded such as in conditions of poor
illumination, low texture, or presence of obscurants including fog, smoke and
dust, the reliability of visible light cameras deteriorates significantly.
Nevertheless, maintaining reliable robot navigation in such conditions is
essential. In contrast to visible light cameras, thermal cameras offer
visibility in the infrared spectrum and can be used in a complementary manner
with visible spectrum cameras for robot localization and navigation tasks,
without paying the significant weight and power penalty typically associated
with carrying other sensors. Exploiting this fact, in this work we present a
multi-sensor fusion algorithm for reliable odometry estimation in GPS-denied
and degraded visual environments. The proposed method utilizes information from
both the visible and thermal spectra for landmark selection and prioritizes
feature extraction from informative image regions based on a metric over
spatial entropy. Furthermore, inertial sensing cues are integrated to improve
the robustness of the odometry estimation process. To verify our solution, a
set of challenging experiments were conducted inside a) an obscurant filed
machine shop-like industrial environment, as well as b) a dark subterranean
mine in the presence of heavy airborne dust.
