Which outcome occurs when Kalman filter tuning errors yield underestimated process noise variances within an autonomous robot's INS?

💡 Explanation

Underestimated process noise causes the *Kalman Gain* to overweight the robot's erroneous INS readings, because the algorithm incorrectly assumes system states are very accurate. Therefore, state estimate diverge increases; rather than sensor fusion improving or irrelevant encoder drift, suboptimal sensor tuning introduces large state-estimation inaccuracies.

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