Sensor selection: UAVs

1. Engineering problem

UAV: the sensor set determines what the robot can perceive, how it localises and how it fails. Choosing it from the failure cases backwards is cheaper than discovering them in the field.

2. When is this needed?

• Starting a new uav design.
• Replacing or adding a sensor after a field failure.
• Reviewing a sensor architecture before purchase.

3. Selection and design decisions

Typical combinations

• GNSS + IMU + barometer + magnetometer: The classic flight-controller sensor set.
• Camera + IMU (+ barometer): Visual-inertial navigation where GNSS is unavailable; weight-light.
• LiDAR + IMU: Geometry-based navigation in low-texture or dark spaces; mind payload and power.

Sensor types that do not fit this application

• RGB-D (active depth) camera: Not typical for this application.
• Wheel / joint encoder: Not typical for this application.
• Force/torque sensor: Not typical for this application.
• Tactile sensor: Not typical for this application.
• UWB (ultra-wideband) ranging: Not typical for this application.
• Sonar (underwater acoustic): Underwater acoustic sensor; not for operation in air.

Try your own requirements in the Sensor Selection Calculator; read the per-sensor detail in the family guides for cameras, LiDAR, inertial and positioning and other sensors.

4. Advantages and disadvantages

• IMU (accelerometer + gyroscope): High rate, no external dependency, works anywhere; but integrates noise and bias into rapid drift.
• GNSS (multi-constellation): Absolute global position, no drift; but metre-level accuracy without corrections.
• Barometer / pressure sensor: Cheap, small, fast; good short-term relative altitude; but weather and temperature shifts look like altitude changes.

5. Requirements and constraints

• Range, accuracy and rate requirements written as numbers before shortlisting.
• Mass, power and compute budgets (see the power and compute guides).
• Environment: lighting, weather, surfaces (glass, reflective, dark).
• Interfaces and ROS 2 driver availability for your distribution.
• Cost and supply for the quantities you need.

6. Architecture

IMU (accelerometer + gyroscope)  ·  GNSS (multi-constellation)  ·  Barometer / pressure sensor
   │ time-synchronised, calibrated extrinsics
   ▼
state estimation / SLAM  ──▶  planning and control

7. Implementation

Integration complexity: High: weight, power and vibration limit every choice, and fusion and timing errors have flight-critical consequences.

1. Select the core sensors and write down what each cannot see.
2. Add the common sensors that cover those gaps.
3. Define time synchronisation and calibration for every sensor pair that is fused (Modules 6 and 7).
4. Define the redundancy and failure behaviour (below), then build and validate sensor by sensor.

Redundancy: Use more than one IMU on isolated mounts, combine barometer and GNSS for altitude, and keep a vision or LiDAR based fallback for GNSS-denied flight; define the failsafe (hold, land, return) for each sensor loss.

8. Real examples

A survey multicopter: GNSS + IMU + barometer + magnetometer on the flight controller, plus a downward camera and a small rangefinder for landing and GNSS-denied segments.

9. Failure scenarios

• GNSS loss or jamming
• Propeller vibration saturating or biasing the IMU
• Barometer disturbed by propwash and sunlight heating
• Camera motion blur and rolling-shutter skew
• Magnetometer corrupted by motor currents

10. Troubleshooting

1. Reproduce the failure and record all sensor streams (ROS bag).
2. Check which sensor lost data or went wrong first (rates, stamps, covariance).
3. Compare against the failure list above and the per-sensor failure scenarios in the family guides.
4. Add the missing modality or monitor, then repeat the test.

11. Validation

• Each listed failure condition is provoked deliberately (glass, sun, GNSS loss, slip) and the robot behaves as specified.
• Each sensor’s data rate, stamps and extrinsics are verified on the integrated robot.
• Redundancy is tested by unplugging each sensor in turn.
• Results are saved with the configuration.

12. Engineering checklist

• Failure cases listed before sensors chosen
• Core sensors cover the main task; common sensors cover their blind spots
• Datasheet specifications checked at operating conditions
• Interfaces, power and ROS 2 drivers checked
• Time sync and calibration planned for every fused pair
• Redundancy and failure behaviour defined and tested
• Safety-rated functions identified separately from navigation sensing

13. Further references

• Class-level guidance written from general engineering knowledge; it contains no product specifications. Product-specific numbers must come from the manufacturer’s datasheet for the exact model and operating mode.
• ROS 2 message types: sensor_msgs (jazzy branch).

Related on FixTheRobot

• Complementary Strengths and Weaknesses of Cameras and LiDAR Perception · Lesson