Keyframe-based thermal-inertial odometry.

Autonomous navigation of microaerial vehicles in environments that are simultaneously GPS-denied and visually degraded, and especially in the dark, texture-less and dust- or smoke-filled settings, is rendered particularly hard. However, a potential solution arises if such aerial robots are equipped with long wave infrared thermal vision systems that are unaffected by darkness and can penetrate many types of obscurants. In response to this fact, this study proposes a keyframe-based thermal-inertial odometry estimation framework tailored to the exact data and concepts of operation of thermal cameras. The front-end component of the proposed solution utilizes full radiometric data to establish reliable correspondences between thermal images, as opposed to operating on rescaled data as previous efforts have presented. In parallel, taking advantage of a keyframe-based optimization back-end the proposed method is suitable for handling periods of data interruption which are commonly present in thermal cameras, while it also ensures the joint optimization of reprojection errors of 3D landmarks and inertial measurement errors. The developed framework was verified with respect to its resilience, performance, and ability to enable autonomous navigation in an extensive set of experimental studies including multiple field deployments in severely degraded, dark, and obscurants-filled underground mines.