Thermal & Infrared Imaging for Robots: The Ultimate Guide

Every object above absolute zero glows. A person, a motor bearing, a loose electrical lug, a leaking pipe, a deer standing in a black field at midnight: each radiates infrared energy in proportion to its temperature, and a thermal camera turns that invisible glow into a picture. That is a different physics from every other sensor a robot carries. A LiDAR fires its own light and times the echo; a depth camera triangulates or clocks a pulse; an RGB camera collects reflected visible light. A thermal camera collects nothing it emitted and depends on no external illumination. It reads the radiation the scene emits by virtue of being warm, which is why it works in total darkness, through smoke and light fog, and why it sees a warm body against a cold wall that a visible camera renders as a flat gray rectangle.

This guide is about long-wave infrared (LWIR) thermal imaging as a robotic sensor: how an uncooled microbolometer converts emitted radiation into a temperature map, what the specs on a thermal camera datasheet actually mean, and the two measurement traps (emissivity and reflected temperature) that turn a confident number on the screen into a wrong one. We will get concrete about the parts that dominate robotics (FLIR/Teledyne Boson and Lepton cores, Seek Thermal, Workswell payloads, DJI's thermal-equipped drones) and about where thermal earns its keep: electrical and mechanical inspection, firefighting and search-and-rescue, security and night operations, agriculture, and medical screening. We will also be honest about the limits, because thermal cameras are low-resolution, they cannot see through ordinary glass, and a good radiometric core still costs more than the RGB camera bolted next to it.

The take: a thermal camera measures emitted radiation and infers temperature, so its accuracy is only as good as your knowledge of the surface it is looking at. The microbolometer is a solved, commoditized transducer; the hard part is the physics between the target and the sensor. Emissivity, reflected background, atmospheric loss, and viewing angle each corrupt the temperature reading, and a robot that treats the on-screen number as ground truth will condemn a healthy motor and pass a failing one. Get the radiometry right, fuse the thermal frame with RGB for context, and thermal becomes the sensor that sees the failure before it happens and the person before the robot hits them.

Companion reading: inspection robots, robot sensors, LiDAR & depth cameras, security & surveillance robots, drone/UAV hardware, and agricultural drones.

Table of contents

1. Key takeaways
2. The physics: why warm things glow
3. How a microbolometer actually works
4. Spectral bands: SWIR, MWIR, LWIR and why robots use LWIR
5. Radiometric vs non-radiometric
6. The specs that matter and reading a datasheet
7. Emissivity and the measurement traps
8. Calibration, drift, and the shutter
9. Fusing thermal with RGB
10. Applications: where thermal earns its keep
11. Thermal payloads on drones and quadrupeds
12. The limits: resolution, glass, cost
13. Selecting a thermal camera
14. Frequently asked questions

The physics: why warm things glow

Thermal imaging rests on one fact of physics: any object with a temperature above absolute zero emits electromagnetic radiation, and the amount and color of that radiation are set by the temperature. This is blackbody radiation, and three laws describe it well enough to build a camera on.

The Stefan-Boltzmann law gives the total power radiated per unit area:

Radiant exitance:  M = epsilon * sigma * T^4

  M       = radiated power per unit area (W/m^2)
  epsilon = emissivity (0 to 1, 1 for an ideal blackbody)
  sigma   = 5.67e-8 W/(m^2 K^4)  (Stefan-Boltzmann constant)
  T       = absolute temperature (kelvin)

The fourth-power dependence is the reason thermal cameras have such enormous dynamic range and such good sensitivity to hot things: a target at 600 K radiates (600/300)^4 = 16 times the power of one at 300 K. It is also why the same absolute temperature difference is easier to see when everything is hot: the derivative dM/dT = 4 * epsilon * sigma * T^3 grows with temperature, so a 1 C difference at 500 C produces far more signal contrast than a 1 C difference at 0 C. A thermal camera watching an electrical panel resolves fine detail near a hotspot and struggles to separate two cold objects a degree apart.

The Wien displacement law tells you which wavelength carries the peak of that radiation:

Peak wavelength:  lambda_peak = b / T

  b = 2898 micrometre-kelvin  (Wien constant)

  T = 300 K (room temp)  ->  lambda_peak ~ 9.7 micrometre
  T = 310 K (human body) ->  lambda_peak ~ 9.3 micrometre
  T = 800 K (dull red hot) -> lambda_peak ~ 3.6 micrometre

At the temperatures a robot cares about (people, machinery, the outdoor world, roughly -20 C to +150 C), the emission peaks around 8-14 micrometres, deep in the long-wave infrared. That single result dictates the whole sensor design: to image room-temperature scenes you build a detector tuned to LWIR, and you use lenses made of germanium or chalcogenide glass because ordinary optical glass is opaque there. A camera optimized for glowing-hot targets (furnaces, molten metal, engine exhaust) shifts toward the mid-wave band where those hotter peaks live.

Emissivity is the term that makes thermal a measurement problem rather than a straight readout. A real surface radiates less than the ideal blackbody by the factor epsilon. A matte black surface, human skin, water, painted metal, wood, and most building materials sit at emissivity 0.90 to 0.97, close enough to a blackbody that the camera's default assumption works. Polished or bare metal is the villain: aluminum, copper, and stainless steel can have emissivity of 0.05 to 0.2, so they emit only a fraction of the radiation their temperature implies and make up the difference by reflecting the infrared of their surroundings. The camera cannot tell emitted from reflected. That confusion is the source of most bad thermal measurements, and the emissivity section is where we deal with it in full.

How a microbolometer actually works

Nearly every thermal camera on a robot uses an uncooled microbolometer focal-plane array. Understanding it explains most of the specs and most of the limits.

A microbolometer pixel is a tiny bridge of infrared-absorbing material suspended on thin legs a micron or two above a silicon readout circuit. Incoming LWIR radiation is absorbed by the bridge and heats it by a small fraction of a degree. The bridge material (usually vanadium oxide, VOx, or amorphous silicon, a-Si) has a resistance that changes sharply with temperature, so measuring the pixel's resistance measures how much infrared it absorbed, which maps back to the temperature of whatever scene point the lens focuses onto it. Multiply by hundreds of thousands of pixels on a focal-plane array and you have a thermal image.

The figure that governs a bolometer's sensitivity is the temperature coefficient of resistance (TCR), how many percent the resistance shifts per degree of self-heating:

Fractional resistance change:  dR/R = TCR * dT_pixel

  TCR ~ -2 %/K for VOx and a-Si microbolometers
  dT_pixel = the tiny temperature rise of the pixel bridge

Two design tensions fall straight out of the physics. First, the pixel must be thermally isolated from the substrate: the support legs are made long and thin so absorbed heat raises the bridge temperature rather than leaking away, which is what makes the pixel sensitive. That same isolation gives the pixel a thermal time constant of several milliseconds, which sets the practical frame rate ceiling (an uncooled microbolometer runs comfortably at 30-60 Hz, not thousands). Second, because the detector sits at ambient temperature, its own thermal noise and any drift in the substrate temperature ride directly on top of the signal, which is why the array needs periodic recalibration against a reference (the shutter, covered later) and why the whole assembly's temperature is monitored and compensated.

"Uncooled" is the word that made thermal affordable for robots. The alternative, a cooled photon detector (indium antimonide or mercury cadmium telluride) chilled to around 77 K by a Stirling cryocooler, is far more sensitive and far faster, and it is what long-range military and scientific thermal systems use. It also costs many thousands to tens of thousands of dollars, draws real power, contains a mechanical cooler that wears out, and takes minutes to reach operating temperature. For almost everything a robot does, the uncooled microbolometer wins: no cooler, seconds to start, single-digit watts, and a core the size of a sugar cube. The trade is sensitivity and speed, which for inspection and situational awareness rarely bind.

Rule of thumb: if the spec sheet does not say "cooled," it is an uncooled microbolometer, and its frame rate (30-60 Hz), its NETD (tens of millikelvin), and its need for a calibration shutter all follow from that. Reach for cooled only when you need to freeze fast motion thermally or resolve tiny temperature differences at long range, and budget an order of magnitude more money.

Spectral bands: SWIR, MWIR, LWIR and why robots use LWIR

"Infrared" spans a wide range of wavelengths, and the sub-bands behave so differently that confusing them is a real design error. The infrared a thermal camera uses is emitted by the scene; the near-infrared a depth camera or night-vision illuminator uses is reflected. They are different physics with different sensors.

Band	Wavelength	Dominant signal	Typical detector	Robotics use
NIR (near IR)	0.75-1.0 micrometre	Reflected (needs illumination)	Silicon (same as cameras)	Active depth, night-vision illuminators, ToF. Not "thermal"
SWIR (short-wave)	1.0-2.5 micrometre	Mostly reflected, some hot emission	InGaAs	Moisture/material sorting, seeing through some haze, silicon inspection
MWIR (mid-wave)	3-5 micrometre	Emitted (hot objects)	InSb, HgCdTe (usually cooled)	High-temperature targets, long-range military, gas imaging
LWIR (long-wave)	8-14 micrometre	Emitted (room-temp objects)	Uncooled microbolometer (VOx/a-Si)	The default robotic thermal band

Robots overwhelmingly use LWIR for three reasons. The scene emits its peak there at ambient temperatures (Wien's law), so you get the most signal from the temperatures you care about. The atmosphere has a clean transmission window from roughly 8 to 14 micrometres, so the radiation reaches the lens without being absorbed by air over practical distances. And the uncooled microbolometer is a mature, cheap LWIR detector. MWIR sees hotter targets better and offers sharper images per aperture (shorter wavelength, less diffraction blur), but it almost always requires a cooled detector, which prices it out of routine robotics. SWIR is a reflective band useful for material discrimination and haze penetration, and it is a genuinely different tool from thermal: a SWIR camera in a dark room sees nothing unless you illuminate it, whereas an LWIR camera sees a warm body glowing on its own.

Radiometric vs non-radiometric

This is the single most important product distinction, and it decides what your robot can actually do with the data.

A non-radiometric thermal camera outputs an image: a grid of intensity values, usually mapped to a false-color palette (white-hot, iron, rainbow). Bright means hotter, dark means colder, but there is no calibrated temperature attached to any pixel. Automatic gain control stretches the contrast to whatever range is in the frame, so the same physical temperature can appear as different brightness from frame to frame. Non-radiometric cores are cheaper and are exactly right when the task is detection: is there a warm human in this dark room, is that motor hotter than its neighbors, where is the fire. The robot or the operator reasons about relative heat, and that is enough.

A radiometric thermal camera outputs a calibrated temperature for every pixel. Behind the scenes the camera has a factory calibration that maps detector counts to radiance, then applies your scene parameters (emissivity, reflected temperature, atmospheric transmission, distance) to solve for the true surface temperature. Now the robot can say "the connection is at 87.4 C, alarm threshold is 70 C, flag it." Radiometric data is what makes automated inspection possible: you set numeric thresholds, log absolute temperatures over time, and trend a bearing's temperature across months. The core costs more, the data is heavier (16-bit per pixel rather than an 8-bit display image), and the accuracy of the number depends entirely on getting the scene parameters right.

The practical rule: if a human or a program needs to decide based on how hot, you need radiometric. If the decision is where is the heat or is something warm present, non-radiometric saves money. Many robotic payloads record radiometric data (16-bit R-JPEG or radiometric TIFF) so the temperature can be re-analyzed later with corrected emissivity, which is impossible if only a colorized image was saved.

War story: a solar inspection contractor flew hundreds of hectares of panels with a non-radiometric thermal drone because it was cheaper, colorizing hotspots for the report. The client's warranty claim needed the actual cell temperatures, which the colorized JPEGs could not provide because the gain had auto-stretched differently on every frame. The whole survey had to be reflown with a radiometric payload. The colorful images looked identical on screen; only one of them contained the numbers.

The specs that matter and reading a datasheet

A thermal datasheet leads with resolution and a dramatic temperature range. The specs that decide whether the camera works for your robot are quieter.

Spec	Units	What it means	Why you care
Resolution	pixels (e.g. 640x512)	Focal-plane array size	Sets how small a feature you can resolve; thermal arrays are small, so this binds constantly
NETD	millikelvin (mK)	Smallest temperature difference resolvable above noise	The true sensitivity spec; 30-50 mK is good uncooled, <20 mK is premium
Spectral band	micrometre (e.g. 8-14)	Which IR wavelengths the detector responds to	LWIR for ambient scenes; confirm it is not a SWIR/NIR part sold as "IR"
Pixel pitch	micrometre (12 or 17)	Physical size of each pixel	Smaller pitch = smaller sensor and optics for the same resolution, at some NETD cost
Frame rate	Hz (9, 30, 60)	Frames per second	9 Hz is the export-limited version; 30-60 Hz for smooth motion and moving platforms
Accuracy	+/- C or %	How close the temperature reading is to truth	Typically the greater of +/- 2 C or +/- 2%; this is the radiometric error floor
Temperature range	C	Span of measurable scene temperatures	Multiple gain modes; high-gain for fine near-ambient work, low-gain for hot targets
Lens / FoV	degrees, focal length	Field of view and angular resolution	Sets ground sample distance from altitude; narrow lens = more detail, less coverage
Thermal time constant	ms	Pixel response speed	Limits usable frame rate and causes smear of fast-moving hot objects

NETD is the sensitivity spec that matters, and it is easy to misread. It is the temperature difference at the scene that produces a signal equal to the camera's noise, so a 40 mK NETD means the camera can just distinguish two surfaces 0.04 C apart. Lower is better. Two traps: NETD is quoted at a specified scene temperature (usually 30 C) and a specified lens f-number (usually f/1.0), and both flatter the number. A faster lens gathers more radiation and improves NETD; a slower lens on your actual robot degrades it. And NETD is a difference spec about detecting contrast, distinct from accuracy, which is how correct the absolute temperature is. A camera can have a superb 30 mK NETD and still be +/- 3 C wrong on absolute temperature because of emissivity error. Sensitivity and accuracy are different axes.

Resolution deserves a reality check. A 640x512 thermal array is about 327,000 pixels, which is a "high-resolution" thermal sensor and a fraction of a decade-old phone camera. The consequence is that the number of pixels on target sets your detection and measurement range hard. To measure a target's temperature accurately you generally want at least 3x3 pixels fully on it (so the pixel is not averaging target and background), and to detect a human you might get away with a handful of pixels. Compute the ground sample distance from your lens and range before you trust any measurement: a hotspot smaller than a pixel gets averaged with its surroundings and reads cooler than it is, the thermal version of the mixed-pixel problem.

Rule of thumb: read a thermal datasheet in this order: is it radiometric, what band, what resolution, what NETD (at what f-number), and what accuracy class. The headline "sees -40 to 550 C" almost never binds; pixels-on-target and emissivity almost always do.

Emissivity and the measurement traps

Here is the core discipline of thermal measurement, the part that separates someone who reads the screen from someone who reads temperatures. The camera measures radiation arriving at the lens. That radiation is a sum of three things, and only the first is what you want:

Radiance at the lens = emitted + reflected + transmitted, attenuated by the atmosphere

  W_measured = tau * [ epsilon * W_object(T_obj)
                     + (1 - epsilon) * W_reflected(T_refl) ]
             + (1 - tau) * W_atmosphere(T_atm)

  epsilon = surface emissivity
  tau     = atmospheric transmission (near 1 at short range)
  T_refl  = temperature of the surroundings the surface reflects
  T_atm   = air temperature along the path

For an opaque surface, emissivity plus reflectivity sum to one (epsilon + rho = 1), so a surface that emits poorly reflects strongly. That is the whole trap in one line. To recover the true object temperature the camera solves that equation for T_obj, and it needs you to supply epsilon, T_refl, tau, and distance. Get emissivity wrong and the temperature is wrong, badly.

The emissivity trap. Point a thermal camera at a polished aluminum busbar carrying enough current to run at 90 C and it may read 35 C. Aluminum's emissivity is around 0.05, so it emits only 5% of the radiation its temperature implies; the other 95% of what the camera sees is the busbar reflecting the cool room. If you tell the camera emissivity is 0.95 (the default), it divides the tiny emitted signal by the wrong factor and reports a temperature far below the truth. Two fixes work in the field. Enter the correct emissivity for the material, which requires knowing the material and its surface finish (published tables exist, but real surfaces vary with oxidation and roughness). Or defeat the problem physically: put a patch of high-emissivity material on the target, matte electrical tape (emissivity ~0.95), a dot of flat black paint, or a correction sticker, let it reach thermal equilibrium with the surface, and measure the patch. Inspectors carry rolls of tape for exactly this reason.

The reflected-temperature trap. Even with correct emissivity, a low-emissivity surface mirrors the infrared of its surroundings onto the camera. Stand in front of a stainless panel and you may see your own warm silhouette reflected in LWIR. On a sunny day, sky and sun reflections off metal roofing or panels create phantom hot and cold spots that have nothing to do with the surface temperature. You compensate by entering a reflected apparent temperature (measured by imaging a crumpled piece of aluminum foil, which reflects the ambient IR, placed at the target), and by choosing a viewing angle that does not put a hot or cold source in the reflection path. Measuring shiny surfaces near normal incidence and away from the sun is basic technique.

Viewing angle matters too. Emissivity is highest near normal incidence and falls off at grazing angles (beyond roughly 45-60 degrees off perpendicular), so a surface imaged at a steep angle reads cooler than the same surface imaged head-on. A drone measuring a solar panel from an oblique angle introduces angle-dependent emissivity error on top of everything else, which is why standardized solar surveys specify a near-nadir view.

Atmosphere and distance. Over short indoor ranges tau is near 1 and you can ignore it. Over long outdoor paths, humid air, and especially rain or heavy fog, the atmosphere absorbs and re-emits LWIR, attenuating the target signal and adding the air's own emission. Serious radiometric software lets you enter distance, humidity, and air temperature to correct for it.

Rule of thumb: never trust a temperature off a shiny surface. If you can, put matte tape or paint on it and measure that. If you cannot, enter the real emissivity and reflected temperature, image near normal incidence, and treat the number as an estimate with several degrees of uncertainty. High-emissivity surfaces (painted metal, most non-metals, skin, water) are forgiving; bare metal is a liar.

Calibration, drift, and the shutter

An uncooled microbolometer sits at ambient temperature, so its output drifts as the camera itself warms up, as the sun hits the housing, or as a drone climbs into colder air. Left uncorrected, that drift shows up as a slowly changing offset and as fixed-pattern noise, a faint checkerboard or vignette baked into every frame because no two pixels have identical response.

The standard fix is a flat-field correction (FFC), and the mechanism is the little click you hear from a thermal camera every so often. An internal shutter (a temperature-controlled flag) swings across the sensor to present a uniform, known temperature to every pixel at once. The camera records each pixel's output against that uniform field and computes a per-pixel offset (and sometimes gain) correction, then retracts the shutter. Every frame afterward has that correction subtracted, which flattens the fixed-pattern noise and re-anchors the calibration. FFC fires periodically (every few tens of seconds) and whenever the sensor temperature drifts past a threshold. The cost is a brief freeze, a fraction of a second where the image blanks, which matters if your robot is using the thermal feed for a fast control or safety loop: you must tolerate or schedule those blanks. Shutterless designs exist and use software correction and a well-characterized sensor, trading some accuracy for an uninterrupted stream.

Beyond FFC, radiometric accuracy depends on the factory NUC (non-uniformity correction) and temperature calibration, done by imaging blackbody references at known temperatures across the operating range. This is why radiometric cameras cost more and why accuracy is quoted as a class (the greater of +/- 2 C or 2% is typical for uncooled radiometric cores). For the tightest work, users run their own periodic calibration against a reference blackbody source. And because the sensor's own temperature is part of the equation, letting the camera thermally stabilize for a few minutes after power-on before taking measurements is standard practice, the thermal analog of letting an IMU settle before you trust its bias.

Fusing thermal with RGB

A thermal image tells you how hot; it is poor at telling you what and where on a recognizable scene, because it is low-resolution and strips away the visual texture, text, and color a human or an object detector uses. The standard answer is to pair a thermal camera with an RGB camera and fuse them.

The simplest fusion is picture-in-picture or side-by-side: the operator sees both feeds and correlates them by eye. More useful is MSX-style blending (Teledyne FLIR's Multi-Spectral Dynamic Imaging is the well-known implementation), which extracts high-frequency edge detail from the visible image and embosses it onto the thermal image, so you get thermal color with sharp visible outlines and readable labels. It is a display trick that dramatically improves interpretability without pretending to add real thermal resolution.

For robotics the important fusion is geometric registration: knowing, for each thermal pixel, which RGB pixel and which point in the world it corresponds to. Because the two cameras sit a few centimeters apart with different fields of view and different resolutions, you calibrate the pair (a thermal-visible calibration target, often a board with heated or emissivity-contrasted markers, since a normal checkerboard is invisible in LWIR) to recover the intrinsics of each and the extrinsic transform between them. With that, a detection in RGB (this is a person, this is transformer T-3) can be tagged with the temperature from the aligned thermal pixel, and a hotspot in thermal can be localized on the visible scene and, via the robot's depth sensor and pose, placed in the world map. This is the same sensor-fusion and TF-tree discipline that governs LiDAR and depth cameras (see the LiDAR & depth cameras guide and the robot sensors guide): a small calibration or timing error becomes a systematic misregistration that puts the temperature label on the wrong object.

Applications: where thermal earns its keep

Thermal imaging is a niche sensor that is indispensable in its niche. The applications share a signature: the information is carried by temperature, and it is invisible or ambiguous to a normal camera.

Electrical inspection

The flagship use. Loose connections, overloaded conductors, failing breakers, and unbalanced phases all dissipate extra power and run hot before they fail. A thermal camera turns that heat into a picture: a hotspot at a lug or a phase running warmer than its siblings flags a fault weeks before it causes an outage or a fire. Utilities, data centers, and industrial plants run scheduled thermal surveys of switchgear, transformers, and busbars. The discipline is comparative (a healthy phase versus a hot one) and quantitative (absolute temperature against a threshold), and the emissivity trap bites hard because so much electrical hardware is bare or plated metal, hence the tape-and-paint technique and the practice of imaging insulated or painted surfaces where possible.

Mechanical inspection

Friction and electrical loss become heat. Overheated bearings, misaligned couplings, overloaded or single-phasing motors, slipping belts, and blocked cooling all show a thermal signature before a catastrophic failure. Condition-monitoring programs trend the temperature of the same bearing housing over months, and a rising trend triggers maintenance. Steam traps, heat exchangers, and refractory-lined vessels reveal blockages and insulation failures as thermal patterns. See the inspection robots guide for how this becomes an autonomous routine.

Building envelope and energy

Thermal reveals where a building leaks heat: missing insulation, thermal bridges, air leaks around windows, and moisture in walls (wet insulation has a different thermal mass and evaporative cooling signature). Roof surveys find trapped moisture under membranes. This is a large commercial-drone market and a growing indoor-robot one.

Firefighting and search-and-rescue

Thermal sees through smoke that blinds a visible camera, so firefighters use handheld and robot-mounted thermal to find people, locate the seat of a fire behind walls, and navigate a smoke-filled building. Search-and-rescue robots and drones scan collapsed structures, water, and wilderness for the warm signature of a human against a cold background, at night and in conditions where visible search fails. The task is detection, so non-radiometric is often adequate, though radiometric helps distinguish a living person from warm debris.

Security, surveillance, and night operations

A warm body stands out against a cool background in total darkness with no illumination, which is why thermal is a mainstay of perimeter security and night surveillance. Unlike near-infrared night vision, thermal needs no IR illuminator to give away its position and is not fooled by camouflage that only works in visible light. Security robots and fixed installations use thermal to detect intruders, and the long detection range (a human is a strong LWIR emitter) makes it valuable for wide-area monitoring. See the security & surveillance robots guide.

Agriculture

Plant canopy temperature is a proxy for water stress: a well-watered plant transpires and cools itself, a stressed plant closes its stomata and warms up. Thermal maps from drones reveal irrigation problems, blocked emitters, and stress zones field by field, often fused with multispectral (NDVI) data for a fuller picture of crop health. Thermal also finds livestock at night and detects fever in herds. See the agricultural drones guide.

Medical and biological screening

Skin temperature and its distribution carry medical information: inflammation, circulation problems, and fever raise local temperature. Elevated-body-temperature screening (deployed widely at building entrances during the COVID period) uses thermal cameras, often with a blackbody reference in frame for accuracy, to flag people with a raised facial temperature. The caveats are severe, skin temperature is not core temperature and emissivity and environment confound it, so medical thermal works as a screening and research tool that flags candidates for a real measurement and leaves diagnosis to a clinical instrument.

Gas detection

Certain optical-gas-imaging cameras (usually cooled MWIR tuned to a specific absorption band) make methane, SF6, and other gases visible as they absorb or emit at their characteristic wavelengths. This is a specialized and expensive corner of thermal imaging used for leak detection on pipelines and in refineries, increasingly flown on drones.

Thermal payloads on drones and quadrupeds

Thermal is one of the two or three payloads that justify a robot going somewhere a human would rather not, which is why it is standard on inspection drones and increasingly on legged robots.

On drones, thermal rides in a gimballed payload, almost always paired with an RGB camera and often a laser rangefinder or zoom. DJI's enterprise line (the thermal-equipped Matrice and Mavic payloads) and dedicated integrators like Workswell and Teledyne FLIR build radiometric-plus-visible gimbals that stabilize the thermal core against the aircraft's motion, geotag every frame, and stream both feeds to the operator. The workflows are mature: solar-farm surveys fly a grid at fixed altitude and near-nadir angle so every panel is imaged consistently and cell-level hotspots are located by GPS; powerline and substation inspection finds hot joints and failing insulators; roof and facade audits map moisture and insulation; and search flies a pattern over terrain looking for human signatures. The constraints are the ones from the drone/UAV hardware guide: payload mass and power cut endurance, gimbal stabilization must hold the low-resolution thermal frame steady enough that a few pixels on target do not smear, and altitude sets the ground sample distance, so measuring a small hotspot forces a lower flight or a narrower lens.

On quadrupeds and ground robots, thermal goes on the sensor mast or a pan-tilt head for autonomous inspection routes: a robot dog walks a substation or a process plant on a schedule, stops at each asset, and captures a radiometric thermal image from a repeatable pose so the temperature trends cleanly over time. Legged platforms reach places wheels cannot and can position the camera at a consistent standoff and angle, which matters for emissivity and for pixels-on-target. The inspection robots guide covers the autonomy and route-repeatability side; the sensor-side lesson is that a fixed, repeatable viewpoint is worth as much as a better camera, because it holds emissivity, reflected temperature, and angle constant across visits so a temperature rise reflects a real change in the asset while the geometry stays fixed.

Payload integration echoes the rest of robotic sensing: the thermal core streams over USB, MIPI-CSI, or GigE; radiometric data is heavier than a display image and wants storage and bandwidth budget; the frame rate may be export-capped at 9 Hz; and the whole thing needs mechanical isolation from vibration, which smears an already low-resolution image.

The limits: resolution, glass, cost

Thermal is powerful and narrow, and being honest about the limits prevents the classic mistake of expecting it to be a night-vision RGB camera.

Low resolution. A high-end uncooled thermal array is 640x512; many robotic cores are 320x256 or smaller. That is coarse. You cannot read a serial number, recognize a face reliably, or resolve fine geometry. Every measurement is constrained by pixels-on-target, and detection range for a given object is set by how many pixels it subtends. This is the reason thermal is almost always fused with a higher-resolution RGB camera that supplies the detail and context.

No vision through glass or water. Ordinary glass is opaque to LWIR and partly reflective, so a thermal camera pointed at a window images the glass itself and whatever the glass reflects, not the room behind it. You cannot use thermal to see into a car through the windshield or through a pane of glass in a door. Water is likewise opaque in LWIR, so thermal does not see below a water surface (it reads the surface temperature). Thin plastic films, some plastics, and smoke are partially transparent and vary case by case. Germanium and chalcogenide are the materials that are transparent in LWIR, which is why thermal lenses are made of them and why they are expensive.

Cost. A radiometric LWIR core with decent resolution and NETD is a real expense, more than the RGB camera it sits beside, because of the specialized detector, the germanium optics, and the calibration. Cooled MWIR and optical-gas-imaging systems climb into many thousands to tens of thousands. Prices have fallen steadily (small Lepton-class cores brought basic thermal to phones and hobby robots), but a measurement-grade payload is still a significant line item.

Slow to stabilize and drift-prone. The uncooled sensor needs warm-up and periodic FFC shutter events, so the stream is not perfectly continuous and absolute accuracy needs a settled camera.

It measures surface, not core. Thermal reads the outside temperature of the nearest opaque surface. A hot component behind a cool cover reads as the cover. Insulation, paint, and coatings all sit between the camera and the thing you care about.

Export controls. High frame rates and high sensitivity historically fall under export regulation (the 9 Hz frame-rate cap on consumer thermal cameras is the visible consequence), which can constrain which cores you can buy, ship, or fly across borders.

Selecting a thermal camera

Choose in roughly this order, each answer narrowing the field before the next.

1. Radiometric or not. Does anything downstream need an absolute temperature (thresholds, trending, reports)? Then radiometric, and accept the cost and the 16-bit data. If the task is pure detection (find the warm person, find the fire, spot the relatively hot motor), non-radiometric is cheaper and simpler.
2. Resolution and pixels-on-target. Work backward from the smallest feature you must measure or detect and your standoff distance. Compute the ground sample distance from the lens focal length and range, and demand at least a few pixels on the smallest target you must measure (more than for mere detection). This usually forces the resolution and lens choice together.
3. NETD. For fine near-ambient work (building envelope, agriculture, medical, subtle mechanical trends) you want low NETD (30-50 mK or better). For gross hotspots (electrical faults, fire) sensitivity is rarely the binding constraint. Read the NETD at the f-number and scene temperature quoted, and derate for your actual lens.
4. Lens and field of view. Wide FoV for coverage and situational awareness, narrow FoV for detail and range. On a drone this trades directly against how low you must fly. Interchangeable lenses exist on higher-end cores.
5. Frame rate. 9 Hz for the export-friendly, cost-sensitive, static-inspection case; 30-60 Hz when the camera or the scene moves, for security, for firefighting, or when it feeds a control loop.
6. Accuracy class. For measurement, the greater of +/- 2 C or 2% is typical; if you need better, you are into premium radiometric cores and disciplined emissivity and reflected-temperature control, possibly with an in-frame blackbody reference.
7. Interface and integration. USB, MIPI-CSI, GigE, or an analog/HDMI video out; a ROS 2 driver or an SDK; radiometric data format (R-JPEG, radiometric TIFF, raw 16-bit); and whether it plays into your gimbal, storage, and time-sync scheme. Budget the integration as first-class engineering, the same as any other sensor.

Representative cores and payloads as of 2026, always confirm current specs against the datasheet:

Product	Class	Typical resolution	Radiometric	Notes
Teledyne FLIR Lepton	Tiny core	160x120 / 80x60	Some variants	Phone/hobby/small-robot scale, low cost
Teledyne FLIR Boson / Boson+	OEM core	320x256 / 640x512	Radiometric variants	The workhorse integration core, many lens options
Seek Thermal cores	OEM core	up to 320x240+	Some variants	Low-cost integration alternative
Workswell WIRIS / drone payloads	Gimbal payload	up to 640x512	Radiometric	Inspection-focused drone payloads, RGB fusion
DJI thermal payloads (Matrice/Mavic)	Gimbal payload	640x512 class	Radiometric	Integrated enterprise drone thermal + RGB + zoom
Cooled MWIR / OGI systems	Specialized	varies	Radiometric	Fast, sensitive, gas imaging; costly, cryocooled

Rule of thumb: the sensor that is radiometric, has enough pixels on your target, and mounts where you can hold a repeatable viewpoint beats a higher-spec core used carelessly. Thermal rewards discipline (known emissivity, controlled angle, settled camera, fused RGB context) more than it rewards raw specifications.

Frequently asked questions

Can a thermal camera see in complete darkness? Yes, and this is its defining strength. Thermal reads the infrared a scene emits because it is warm, so it needs no light of any kind. A warm human, animal, or machine glows against a cooler background at midnight exactly as it does at noon. This is different from near-infrared night vision, which is reflective and needs an IR illuminator; thermal is fully passive.

What is the difference between thermal and infrared night vision? Night vision (image intensifiers or NIR cameras) amplifies faint reflected light, including near-infrared, and needs some ambient light or an IR illuminator. It gives a recognizable, high-resolution picture. Thermal (LWIR) images emitted heat, works in zero light and through smoke, and shows temperature, but at low resolution and without fine visual detail. They are complementary tools: night vision for recognition, thermal for detection and heat.

Why does a shiny metal part read the wrong temperature? Emissivity. Polished metal emits only a small fraction of the radiation its temperature implies (emissivity near 0.05-0.2) and reflects its surroundings instead. The camera, assuming a high emissivity, reads the small emitted signal as a low temperature and adds the reflected room on top. Fix it by entering the correct emissivity, or by taping or painting a matte high-emissivity patch on the part and measuring that.

Can thermal cameras see through walls or glass? No. Thermal reads the surface temperature of the nearest opaque object. Ordinary glass is opaque and reflective in LWIR, so you image the glass and its reflections, not what is behind it. Walls are opaque too; you may see a warm patch where heat has conducted through, but not the object on the other side. Thermal does not see through solid barriers.

Radiometric or non-radiometric, which do I need? Radiometric if any decision depends on an absolute temperature: alarm thresholds, trending a bearing over months, warranty-grade reports. Non-radiometric if the task is detection or relative comparison: find the warm person, spot the hotter-than-its-neighbors motor, locate the fire. Radiometric costs more and produces heavier 16-bit data. When in doubt, record radiometric so you can reanalyze later.

What resolution do I actually need? Work from pixels-on-target. To measure a temperature accurately you want at least a few pixels fully on the target so it is not averaged with the background; to merely detect a warm object you can use fewer. Compute the ground sample distance from your lens and range. A 640x512 array is high-end thermal; 320x256 is common; below that you are limited to close-range or large targets.

What is NETD and what number is good? NETD (noise-equivalent temperature difference) is the smallest temperature difference the camera can resolve above its own noise, in millikelvin. Lower is better. Good uncooled cores hit 30-50 mK, premium and cooled cores go below 20 mK. It is a sensitivity spec about detecting contrast, separate from absolute accuracy (typically the greater of +/- 2 C or 2%), which emissivity error dominates.

Why does my thermal camera click and briefly freeze? That is the flat-field-correction shutter. An internal flag swings across the sensor to present a uniform temperature so the camera can recalibrate each pixel's offset and remove fixed-pattern noise and drift. It fires periodically and when the sensor temperature changes. The brief image freeze is normal; if your robot uses thermal in a fast loop, plan for those blanks or choose a shutterless core.

Does thermal work in rain, fog, or smoke? Smoke: yes, thermal sees through most smoke that blinds a visible camera, which is why firefighters rely on it. Light fog and haze: usually better than visible, though heavy fog and rain absorb and scatter LWIR and cut range. Water on the lens or heavy precipitation degrades it. Thermal is more weather-robust than visible for detection but not immune.

Why is thermal so much more expensive than a regular camera? The detector (an uncooled microbolometer array) is a specialized MEMS device, the lenses must be germanium or chalcogenide because ordinary glass is opaque in LWIR, and radiometric cores require factory calibration against blackbody references. Cooled MWIR systems add a cryocooler. Prices have fallen, and small cores are now affordable, but a measurement-grade payload remains a significant cost.

Can I fly a high-frame-rate thermal camera anywhere? Not always. High frame rates and high sensitivity fall under export-control regulations in many jurisdictions, which is why many consumer thermal cameras are capped at 9 Hz. If you need 30-60 Hz thermal, check the export classification of the core and any restrictions on shipping or operating it across borders.