Filter Array

Description

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.

Properties

• 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 Evaluate is 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).

Inputs

Outputs

Example

Coming soon.