Sensor selection: outdoor ground robots

1. Engineering problem

Outdoor ground robot: 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 outdoor ground robot design.
• Replacing or adding a sensor after a field failure.
• Reviewing a sensor architecture before purchase.

3. Selection and design decisions

Typical combinations

• GNSS (RTK) + IMU + wheel encoders: Absolute outdoor pose with dead reckoning between fixes.
• GNSS + IMU + 3D LiDAR: LiDAR odometry and mapping bridges GNSS outages near structures.
• Camera + 3D LiDAR + radar: Redundant perception across lighting and weather.

Sensor types that do not fit this application

• RGB-D (active depth) camera: Not typical for this application.
• Barometer / pressure sensor: 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

• 3D LiDAR (mechanical, spinning): Full 3D geometry with wide field of view and direct metric range; but high cost, power and point-cloud processing load.
• 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.
• Wheel / joint encoder: Precise, high-rate proprioception; basis of odometry and joint control; but odometry drifts from wheel slip and uncertain wheel radius/track.

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

3D LiDAR (mechanical, spinning)  ·  IMU (accelerometer + gyroscope)  ·  GNSS (multi-constellation)  ·  Wheel / joint encoder
   │ time-synchronised, calibrated extrinsics
   ▼
state estimation / SLAM  ──▶  planning and control

7. Implementation

Integration complexity: Medium to high: GNSS, 3D LiDAR and an IMU must be time-synchronised and calibrated, and weather protection adds mechanical work.

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: Keep two independent localisation sources (GNSS-based and LiDAR-inertial) and a monitor that decides which to trust; add radar where weather degrades optical sensing; use dual GNSS antennas if heading is needed while stationary.

8. Real examples

A campus delivery rover: RTK GNSS + IMU + wheel encoders outdoors, a 3D LiDAR for mapping and obstacle sensing, and a radar for rain.

9. Failure scenarios

• GNSS multipath near buildings and under trees
• Rain, spray and dust creating false LiDAR points
• Direct sun saturating cameras
• Wheel slip on loose or wet ground
• Vibration degrading the IMU

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