Warning
You are reading the documentation for an older Pickit release (2.4). Documentation for the latest release (3.4) can be found here.
Explaining the Teach detection parameters
The Pickit Teach detection engine is designed to detect complex 3D shapes. This article explains its detection parameters.
HD camera settings (HD camera only)
The HD camera settings are explained in HD camera settings (HD camera only).
Define models
A list of taught models is shown accompanied by their ID and the number of points. By default, there are no models defined.
Add a model
Press Teach from camera or Teach from CAD to teach your first model. The points currently visible within the ROI are now saved into a model. When the model is successfully created, the model will automatically be shown in the tab.
To add extra models, press + in the model tab. In one product file, up to 8 different models can be taught. This means that Pickit Teach is capable of looking for 8 different shapes in one detection. See How to make a robot program with multiple models on how you can use the model ID in a robot program.
Re-teach a model
A model taught from the camera can be retaught by pressing Re-teach. By pressing this button, the points currently visible within the ROI are saved into the model.
Warning
When re-teaching a specific model, the point-cloud data from the previous model is overwritten. This action cannot be undone.
It is also possible to crop or expand an existing model, without the need to re-teach it. Refer to the article How to edit an existing Pickit Teach model for further details.
Delete a model
To delete a model, press Delete for the specific model. The action needs to be confirmed in a popup.
Warning
Model ID’s will be reassigned after deleting a model. If you use model ID’s in your robot program, make sure they’re up to date after deleting models.
Warning
Model data will be lost after deletion confirmation and cannot be restored.
Select and visualize a model
A model can be selected by clicking on the respective tab. The open model can then be visualized in a specific window inside the tab.
Enable or disable a model
A model is enabled by default, and can be disabled through the toggle switch at the left of the respective tab. If a model is disabled, it will be ignored in subsequent detection runs. Disabling models is a quick and useful way to check the effect of different combinations of models, or to test distinct models in isolation (by disabling all others).
Matching tolerance
If the distance between a detected scene point and a point of your model is below this position tolerance value, then this scene point will confirm the model point. This parameter has a big impact on the scoring of the Minimum matching score.
The following guide will help you find a good value for this parameter, How to set good model matching parameters.
Minimum matching score
Minimum percentage of model points that need to be confirmed by scene points, for the detected object to be considered valid.
Note
For CAD models, only the points that could be visible from the camera are used in the computation of the matching score.
The following guide will help you find a good value for this parameter, How to set good model matching parameters.
Optimize detections
Flat objects
Detect objects based on their edges. This feature is especially useful for flat objects, like sheet metal plates. When enabled, an additional input to specify the object thickness is needed.
Image fusion and scene downsampling resolution
Image fusion (applicable for the M and L cameras only) and scene downsampling resolution are explained in Optimize detections.
Detection speed
With this parameter, you can specify how hard Pickit Teach tries to find multiple matches. Slower detection speeds are likely to produce more matches. There are three available options:
Fast Recommended for simple scenes with a single or few objects.
Normal This is the default choice and represents a good compromise between a number of matches and detection speed.
Slow Recommended for scenes with many parts, potentially overlapping and in clutter.
Example: Two-step bin picking.
Pick an individual part from a bin using Normal or Slow detection speed and place it on a flat surface.
Perform an orientation check for re-grasping using Fast detection speed, as the part is isolated. Grasp and place in final location.