What Is Sensor Fusion?

Sensor fusion is the process of combining measurements from several sensors into a single estimate of the world that is more accurate, more complete and more reliable than any one sensor could give. It is how an autonomous vehicle knows where it is and what is around it, how a drone holds its position in wind, and how a robot finds its way around a warehouse.

The reason is simple: each sensor has strengths and blind spots. Cameras see detail and colour but struggle in poor light and cannot measure distance directly. LiDAR measures distance precisely but sees little detail and suffers in heavy rain. Radar works in rain and fog and measures speed directly, but with coarse resolution. GNSS gives absolute position outdoors and nothing in a tunnel. An IMU tracks motion smoothly but drifts over time. Fusing them covers each sensor’s weaknesses with another’s strengths.

Fusion also adds redundancy. If one sensor fails, is blocked by mud or is dazzled by low sun, the others keep the system working, perhaps at reduced speed, rather than leaving it blind. For machines that move near people, that graceful degradation is often a safety requirement, not just a performance gain, and it is one reason safety-rated systems rarely rely on a single type of sensor.

The table summarises what each common sensor contributes to a fused system and where it needs help.

The most common pairings follow from the table: camera and LiDAR for detailed 3D perception, camera and radar for perception that keeps working in bad weather, and GNSS with an IMU for position that holds through brief signal loss.

Fusion can happen at different stages of the processing pipeline, and the choice affects accuracy, bandwidth and how easily sensors can be swapped.

The classic tool is the Kalman filter. It keeps a running estimate of a state, such as position and velocity, predicts how it will change, then corrects the prediction with each new measurement, weighting every sensor by how much it is trusted. Extended and unscented Kalman filters handle non-linear motion, and particle filters handle messier cases. A GNSS and IMU navigation system, or a drone’s flight controller, is usually built around one of these.

Perception increasingly uses deep learning, where a neural network takes camera images and LiDAR or radar data together and outputs objects in 3D. These models need a GPU or AI accelerator on the vehicle. Our glossary of AI GPU computing terms covers the wider vocabulary.

Fusion fails quietly if the data does not line up. Every measurement needs an accurate timestamp, because a vehicle moving at 20 m/s travels 2 cm in a millisecond. Systems use hardware triggers, GNSS pulse-per-second signals and IEEE 1588 Precision Time Protocol to keep sensors and the computer on one clock; our guide to synchronising machine vision with PTP explains how.

Calibration is the other half. Intrinsic calibration corrects each sensor’s own distortion; extrinsic calibration measures exactly where each sensor sits and points relative to the others. Both drift if a sensor is knocked, so rugged mounting and periodic checks matter.

Fusion needs a computer with the inputs and the AI performance to process every stream at once: several cameras, often over GMSL for long, locking cable runs, Ethernet for LiDAR and radar, CAN for vehicle data, and serial or USB for GNSS and the IMU. Our autonomous tractor case study fuses eight GMSL2 cameras, PoE+ LiDAR and RTK on a Jetson AGX Orin computer, and our GMSL cameras pair with Jetson systems built for this kind of work.

We supply edge AI computers for sensor fusion with the camera, LiDAR, GNSS and time sync interfaces set up as standard, and test every input before the system ships. Send us your sensor list and we will specify the computer to match.