  State estimation in complex illumination environments based on conventional
visual-inertial odometry is a challenging task due to the severe visual
degradation of the visual camera. The thermal infrared camera is capable of
all-day time and is less affected by illumination variation. However, most
existing visual data association algorithms are incompatible because the
thermal infrared data contains large noise and low contrast. Motivated by the
phenomenon that thermal radiation varies most significantly at the edges of
objects, the study proposes an ETIO, which is the first edge-based monocular
thermal-inertial odometry for robust localization in visually degraded
environments. Instead of the raw image, we utilize the binarized image from
edge extraction for pose estimation to overcome the poor thermal infrared image
quality. Then, an adaptive feature tracking strategy ADT-KLT is developed for
robust data association based on limited edge information and its distance
distribution. Finally, a pose graph optimization performs real-time estimation
over a sliding window of recent states by combining IMU pre-integration with
reprojection error of all edge feature observations. We evaluated the
performance of the proposed system on public datasets and real-world
experiments and compared it against state-of-the-art methods. The proposed ETIO
was verified with the ability to enable accurate and robust localization
all-day time.
