This node applies a Kalman Filter on every item in an array, affecting only the X, Y and Z properties.
It can help filter out noise induced by the sensor in the detection of objects or people in it’s field of view.
Process noise: Affects noise reduction in the predictions of the algorithm. (See below)
Default : 0.50
Measurement noise: Affects noise reduction in the measures within the algorithm. (See below)
Default : 0.50
Error cov. post: Affects uncertainty in the initialisation of the algorithm. If your starting position is accurate, prefer lower values. If not, prefer higher values.
Default : 1.0
Delay: Useful when compensating for projector input lag. Leave at 0 otherwise. Unit : milliseconds
Default : 0.0
Force Evaluate: When toggled ON, will evaluate a prediction even if the input does not change.
Default : OFF
Interpolation: (Only visible when
Force Evaluateis OFF) Adds an interpolation to the values based on the algorithm. Unit : milliseconds
Default : 0
Predictions / Measures noise
Process noise and Measurement noise are complimentary properties. One should be rather low while the other is rather high :
- If Process noise is low : we trust the predictions to be accurate, Measurement noise can be higher to reduce noise in our measures.
In this case the output will be smoother but may have a higher latency (delay).
- If Measurement noise is low : we trust our measures to be accurate, Process noise can be higher to reduce noise in the predictions.
In this case the output will have a lower latency but may be noisier (jerk).
|Array||Array||Array with items having X, Y and/or Z properties to be filtered|
|Array||Array||Array with filtered values on X, Y and/or Z properties|
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