LEARN / TECHNOLOGY
Sensor Fusion
Sensor fusion combines measurements from multiple sensors to estimate a system or environment more reliably than a single stream alone.
How it works
Probabilistic filters, optimization or learned models reconcile measurements with different noise, fields of view and update rates.
Why it matters
Autonomous systems commonly need complementary inputs from cameras, lidar, radar, inertial sensors and positioning systems.
Advantages
- complementary sensing
- fault tolerance opportunities
- improved state estimation
Limitations
- calibration and synchronization
- correlated errors
- complex failure analysis
See it in the Observatory
A definition is a starting point.
Capabilities depend on implementation, operating environment, training data, hardware and safety controls. Follow the connected records and sources before generalizing.