Sensor Fusion

Sensor Fusion
Combining observations from multiple sensor sources

Sensor fusion is the process of combining measurements or estimates from multiple sensors to obtain a more useful description of a system or environment. Sensors have different strengths and failure modes; their measurements can also be noisy, delayed, or incomplete. 1(#cite_note-1)

Sensor combinations

A mobile robot may combine wheel odometry, inertial measurements, visual observations, and position estimates. A manipulator might use vision together with joint encoders and force sensing. The particular sensor mix depends on the task and operating conditions.

Methods

Methods range from rule-based aggregation to probabilistic estimation, state observers, optimization, and machine-learning techniques. Implementations must account for calibration, time synchronization, uncertainty and conflicting measurements.

Limits and reliability

Fusion is not guaranteed to improve accuracy: inaccurate calibration, correlated errors and spoofed inputs can degrade results. Engineering evaluation should compare performance under lighting changes, occlusion, motion or sensor faults. In physical AI, the estimated state informs planning and control.

Further reading

• [AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) — NIST

See also

• Sensors
• Machine learning
• Computer vision
• Edge AI
• Autonomous systems

References

1. [^](#cite_ref-1 "Jump up") Untitled web source.

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