TP-TIO: A Robust Thermal-Inertial Odometry with Deep ThermalPoint

Shibo Zhao

Abstract:

To achieve robust motion estimation in visually degraded environments, thermal odometry has been an attraction
in the robotics community. However, most thermal odometry methods are purely based on classical feature extractors, which
is difficult to establish robust correspondences in successive
frames due to sudden photometric changes and large thermal
noise. To solve this problem, we propose ThermalPoint, a
lightweight feature detection network specifically tailored for
producing keypoints on thermal images, providing notable
anti-noise improvements compared with other state-of-the-art
methods. After that, we combine ThermalPoint with a novel
radiometric feature tracking method, which directly makes use
of full radiometric data and establishes reliable correspondences
between sequential frames. Finally, taking advantage of an
optimization-based visual-inertial framework, a deep featurebased thermal-inertial odometry (TP-TIO) framework is proposed and evaluated thoroughly in various visually degraded
environments. Experiments show that our method outperforms
state-of-the-art visual and laser odometry methods in smokefilled environments and achieves competitive accuracy in normal environments