Which risk increases when sensor fusion algorithms rely heavily on Kalman filtering, processing noisy IMU data of a robotic arm joint?

💡 Explanation

Unbounded filter divergence poses significant risk to reliant robots utilizing faulty IMU sensor data, since noise accumulates through recursive least squares estimation. The Kalman filter’s covariance estimation loses accuracy quickly under poorly regulated, raw data because, therefore the filter becomes unstable, ultimately leading to unbounded, exponential error rather than convergence with good correction.

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