Thermal-Inertial Navigation Systems for Autonomous Aerial Vehicles
Summary
Thermal-inertial navigation systems integrate thermal imaging sensors with inertial measurement units (IMUs) to enable precise localisation and mapping for unmanned aerial vehicles (UAVs), particularly in environments where GPS signals are unreliable or unavailable. By exploiting differences in scene temperature, thermal cameras provide robust visual cues under conditions of low light, smoke, dust or foliage cover. In parallel, IMUs supply continuous estimates of linear acceleration and angular velocity, ensuring motion tracking even during rapid manoeuvres or temporary sensor occlusion. The fusion of these modalities addresses the drift inherent in pure inertial navigation and the feature scarcity that can hinder conventional optical or LiDAR-based methods. Thermal-inertial systems have demonstrated enhanced resilience in search-and-rescue, environmental monitoring and infrastructure inspection, offering real-time mapping and control feedback in challenging operational theatres. Advances in onboard processing power and algorithmic efficiency have further reduced payload size and energy consumption, paving the way for widespread adoption across military, civil and scientific UAV missions.
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Thermal-Inertial Navigation Systems for Autonomous Aerial Vehicles publication trend
The graph below shows the total number of articles in thermal-inertial navigation systems for autonomous aerial vehicles across all publications each year (not limited to Nature Index journals).
Technical terms
Thermal-Inertial Navigation System: A hybrid approach that merges thermal imaging and inertial sensing to determine an aerial vehicle’s position and orientation without reliance on external positioning signals.
Inertial Measurement Unit (IMU): A sensor assembly, typically comprising accelerometers and gyroscopes, that measures linear acceleration and angular velocity to infer motion.
Thermal Imaging Sensor: A camera that detects infrared radiation, producing images based on temperature differences rather than visible light.
Sensor Fusion: The process of combining data from multiple sensors to produce a more accurate, reliable estimate of state than could be obtained from individual sensors alone.
Simultaneous Localisation and Mapping (SLAM): An algorithmic framework that constructs a map of an unknown environment while concurrently tracking the vehicle’s trajectory within that map.
Optical Flow: A computer-vision technique for estimating apparent motion between successive image frames, used to infer velocity and displacement.
References
- A Review of Modern Thermal Imaging Sensor Technology and Applications for Autonomous Aerial Navigation. Journal of Imaging (2021).
- A Comparison of Dense and Sparse Optical Flow Techniques for Low-Resolution Aerial Thermal Imagery. Journal of Imaging (2022).
- 3D Radiometric Mapping by Means of LiDAR SLAM and Thermal Camera Data Fusion. Sensors (2022).
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