Using vision for navigation of airborne systems provides an opportunity for motion relative to the ground to be controlled in the absence of other supporting sensors including global navigation satellite systems. Rather than relying on computationally intensive localization techniques such as online map construction, identification and tracking of landmarks, or otherwise producing an explicit quantitative estimate of position, we propose and have experimentally demonstrated a closed-loop visual navigation reflex which we term the optical ground course controller. The behavior is applicable to fixed wing aircraft traversing long ranges, and reduces the online computation and sensors required compared to other visual methods. This method combines the kinematics of fixed wing aircraft flight, the direction of apparent motion of an image sequence, and a magnetic compass to create a bioinspired optomotor reflex similar to those observed in insects. This behavior accurately controls track in the inertial reference frame (path taken over the ground) with only limited dependence on altitude, speed, and wind. We show that the proposed behavior is naturally convergent and stable, and present experimental results from simulation and real-world flight demonstrating that the method performs robustly, producing improvement over both magnetic-referenced and visual odometry-based navigation within the limits of the sensor. K E Y W O R D S embodied autonomy, optical flow, optomotor, UAV

Visual navigation is a commonly researched alternative to the use of global navigation satellite systems in challenging environments where satellite signals are not available. However, the vast majority of visual navigation techniques studied to date require scene illumination of some form. In this study, we use a low‐resolution long‐wave infrared (LWIR) image sensor sensitive to thermal emissivity within an optical flow processing engine to extend a low complexity track‐based navigation scheme for fixed wing aircraft to operate at night. A mixture of closed and open loop flight experiments conducted on a small UAV integrated with the new sensor demonstrate: accurate track‐based navigation in visual darkness; that the LWIR sensor performs equivalently to the benchmark optical flow sensor during daylight and continues to operate in low light; and that the LWIR sensor is able to detect suitable textures for operation at night across a wide span of altitudes. These results demonstrate utility of optical flow algorithms with low‐resolution thermal scenes as a novel aircraft navigation sensor for day and night operation.

Limited navigation capabilities of many current robots and UAVs restricts their applications in GPS denied areas. Large aircraft with complex navigation systems rely on a variety of sensors including radio frequency aids and high performance inertial systems rendering them somewhat resistant to GPS denial. The rapid development of computer vision has seen cameras incorporated into small drones. Vision-based systems, consisting of one or more cameras, could arguably satisfy both size and weight constraints faced by UAVs. A new generation of thermal sensors is available that are lighter, smaller and widely available. Thermal sensors are a solution to enable navigation in difficult environments, including in low-light, dust or smoke. The purpose of this paper is to present a comprehensive literature review of thermal sensors integrated into navigation systems. Furthermore, the physics and characteristics of thermal sensors will also be presented to provide insight into challenges when integrating thermal sensors in place of conventional visual spectrum sensors.