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.

This study explores the utility of optical flow calculated from thermal imaging cameras, "thermal flow," mounted on an aircraft for localization in day and night conditions. Our sensor implementation utilizes a long wave infrared (LWIR) micro sensor to capture sequences of thermal images and an on-board computer to compute an optical flow estimate. We compared the performance of optical flow from the LWIR camera with the output of visible spectrum optical flow sensor.Flights were conducted spanning a 24 h window to explore how thermal flow performs relative to optical flow as the ground heats and cools. Agreement between optical and thermal flow was found during daylight when both sensors were functional. Additionally, thermal flow results were reliable in the middle of the day through to late evening, gradually degrading until shortly after sunrise.

The study explores the feasibility of optical flow‐based neural network from real‐world thermal aerial imagery. While traditional optical flow techniques have shown adequate performance, sparse techniques do not work well during cold‐soaked low‐contrast conditions, and dense algorithms are more accurate in low‐contrast conditions but suffer from the aperture problem in some scenes. On the other hand, optical flow from convolutional neural networks has demonstrated good performance with strong generalization from several synthetic public data set benchmarks. Ground truth was generated from real‐world thermal data estimated with traditional dense optical flow techniques. The state‐of‐the‐art Recurrent All‐Pairs Field Transform for the Optical Flow model was trained with both color synthetic data and the captured real‐world thermal data across various thermal contrast conditions. The results showed strong performance of the deep‐learning network against established sparse and dense optical flow techniques in various environments and weather conditions, at the cost of higher computational demand.

We describe a memory system which uses a molecular information processing primitive to control "reference" neurons. This system has been simulated in the context of an organism navigating in an environment. The system adapts to the environment by developing ensembles of neurons which coordinate sensory input and movement in the environment. The principal learning mechanism is based on synchronous activity of neurons. The frequency and duration of the different neural firing is critical for successful manipulation of memories developed in this system. This study suggests appropriate timing parameters for molecular processing based on the reference neuron.