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Building a Sensor Fusion Pipeline for Cameras, LiDAR, and IMUs

#robotics #ros2 #sensorfusion #lidar

Building a Sensor Fusion Pipeline for Cameras, LiDAR, and IMUs

No single sensor provides a complete representation of a robot's environment.

  • Cameras provide rich visual information.
  • LiDAR provides accurate geometric measurements.
  • IMUs provide high-frequency motion information.

Sensor fusion combines these complementary signals.

Pipeline Architecture 

Camera ---> Image Processing ----+
                                  |
LiDAR ----> Point Cloud ---------+--> Fusion --> State Estimate
                                  |
IMU ------> Preprocessing --------+
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The First Problem: Time

Sensor fusion is fundamentally a temporal problem.

Suppose:

Camera: 10:00:00.100
LiDAR:  10:00:00.120
IMU:    10:00:00.105
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These measurements cannot simply be treated as if they occurred simultaneously.

Use timestamps and synchronization strategies appropriate to your hardware.

The Second Problem: Coordinate Frames

A camera, LiDAR, and IMU have different coordinate frames.

base_link
   |
   +---- camera_link
   |
   +---- lidar_link
   |
   +---- imu_link
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ROS 2 uses TF2 to represent relationships between frames.

A robust system must know:

  • Sensor mounting position
  • Sensor orientation
  • Frame conventions
  • Extrinsic calibration

Camera + LiDAR

A common pipeline is:

3D LiDAR Point
      |
      v
Transform to Camera Frame
      |
      v
Project into Image
      |
      v
Associate with Image Features
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This enables applications such as:

  • 3D object detection
  • Depth completion
  • Semantic point clouds
  • Obstacle classification

IMU Integration

IMUs provide high-frequency measurements of:

  • Angular velocity
  • Linear acceleration

They can help bridge gaps between slower camera observations.

A simplified architecture is:

IMU ----> Prediction
             |
Camera --> Correction
             |
LiDAR ---> Correction
             |
             v
        State Estimate
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This is the basic idea behind many probabilistic state estimation systems.

Practical Fusion Pipeline

A production implementation should include:

  1. Sensor drivers
  2. Timestamp validation
  3. Calibration
  4. TF2 transforms
  5. Synchronization
  6. Sensor preprocessing
  7. Fusion algorithm
  8. Outlier rejection
  9. State publication
  10. Monitoring

Common Failure Modes

Bad Calibration

Even small extrinsic errors can cause projected LiDAR points to appear offset from objects.

Timestamp Drift

Sensors with different clocks can gradually become misaligned.

Frame Confusion

Mixing coordinate conventions can produce apparently impossible motion.

Unbounded Queues

Large queues may increase latency rather than improve reliability.

Validation

Record representative sensor data and replay it.

Measure:

  • Fusion latency
  • Position error
  • Orientation error
  • Sensor dropouts
  • Synchronization error
  • CPU/GPU utilization

A good fusion pipeline is not simply one that combines more sensors. It is one that combines well-calibrated, correctly timestamped measurements in a computationally controlled way.

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