Sensor Fusion
Combining data from multiple sensor types for a richer, more accurate measurement.
Sensor fusion algorithms merge inputs from accelerometers, gyroscopes, cameras, LiDAR, temperature probes, and other sources into a single coherent model. Kalman filters, complementary filters, and neural network approaches are common techniques.
No single sensor tells the whole story. An accelerometer can detect motion but not orientation drift; a camera captures shape but struggles in low light. Fusing them yields reliability and accuracy that no standalone sensor can match — critical for autonomous systems, robotics, and medical devices.
We implement sensor fusion at the firmware level for our IoT and robotics clients. Our embedded engineers select the optimal filter architecture based on power budget, latency requirements, and the physical environment the device will operate in.