Vision Project Configuration

Before using this tutorial, you should have created a solution using the “Single-Case Depalletizing” case project in the Robot Communication Configuration section.

In this tutorial, you will first learn the project workflow, and then deploy the project by adjusting the Step parameters to recognize the cartons’ poses and output the vision result.

Introduction to the Project Workflow

In this tutorial, you need to configure the vision project using Mech-Vision and Mech-Viz. The process of how to configure a vision project is shown in the figure below.

The phases of the vision project configuration process are explained below.

Phase	Used software	Description
Connect to the Camera and Acquire Images	Mech-Vision	Connect to the camera through the Mech-Vision’s “Capture Images from Camera” Step for image capturing purposes.
Preprocess point clouds and obtain the highest-layer 2D images	Mech-Vision	Perform point cloud preprocessing on image data and obtain the highest layer color image through the Mech-Vision’s “Preprocess Point Clouds and Obtain the Highest-Layer 2D Images” custom Procedure.
Deep Learning Inference	Mech-Vision	Use the Mech-Vision’s "Deep Learning Model Package Inference" Step to segment the highest-layer color images.
Calculate carton poses and dimensions	Mech-Vision	You can use the Mech-Vision’s “Calculate Carton Poses and Dimensions” custom Procedure to calculate the poses of the highest-layer cartons.
Adjust Poses	Mech-Vision	Adjust the reference frame and orientation of, sort, or filter poses output by the “3D Target Object Recognition” Step using the “Adjust Poses V2” Step of the Mech-Vision software.
Plan Robot Path	Mech-Vision & Mech-Viz	The “Output” Step of Mech-Vision sends the vision result (such as object center points, scene point clouds, cartons' upper surface dimensions, etc.) to Mech-Viz. The Mech-Viz project dynamically plans a collision-free robot motion path based on the vision result.
Output Planned Path	Mech-Viz	Upon receiving the Standard Interface command from the robot (used in this tutorial) or the PLC, Mech-Viz returns the planned collision-free robot motion path. After completing the robot communication configuration and Mech-Vision project and Mech-Viz project configuration, Mech-Viz can output the planned path after each run. You need to write the robot program to send the Standard Interface commands that trigger the Mech-Viz project to run and obtain the planned path from Mech-Viz. For details, refer to the instructions in the “Picking and Placing” section.

Phase

Used software

Description

Connect to the Camera and Acquire Images

Mech-Vision

Connect to the camera through the Mech-Vision’s “Capture Images from Camera” Step for image capturing purposes.

Preprocess point clouds and obtain the highest-layer 2D images

Mech-Vision

Perform point cloud preprocessing on image data and obtain the highest layer color image through the Mech-Vision’s “Preprocess Point Clouds and Obtain the Highest-Layer 2D Images” custom Procedure.

Deep Learning Inference

Mech-Vision

Use the Mech-Vision’s "Deep Learning Model Package Inference" Step to segment the highest-layer color images.

Calculate carton poses and dimensions

Mech-Vision

You can use the Mech-Vision’s “Calculate Carton Poses and Dimensions” custom Procedure to calculate the poses of the highest-layer cartons.

Adjust Poses

Mech-Vision

Adjust the reference frame and orientation of, sort, or filter poses output by the “3D Target Object Recognition” Step using the “Adjust Poses V2” Step of the Mech-Vision software.

Plan Robot Path

Mech-Vision & Mech-Viz

The “Output” Step of Mech-Vision sends the vision result (such as object center points, scene point clouds, cartons' upper surface dimensions, etc.) to Mech-Viz.
The Mech-Viz project dynamically plans a collision-free robot motion path based on the vision result.

Output Planned Path

Mech-Viz

Upon receiving the Standard Interface command from the robot (used in this tutorial) or the PLC, Mech-Viz returns the planned collision-free robot motion path.
After completing the robot communication configuration and Mech-Vision project and Mech-Viz project configuration, Mech-Viz can output the planned path after each run.
You need to write the robot program to send the Standard Interface commands that trigger the Mech-Viz project to run and obtain the planned path from Mech-Viz. For details, refer to the instructions in the “Picking and Placing” section.

Adjust Step Parameters

In this section, you will deploy the project by adjusting the parameters of each Step.

The project in this section is "Vis-Single-Case Depalletizing" in the “Single-Case Depalletizing” solution.

Capture Images from Camera

Step name	Capture Images from Camera
Phase	Connect to the Camera and Acquire Images
Illustration
Description	Connect to a real camera and configure relevant parameters to ensure that the camera can capture images normally.

Step name

Capture Images from Camera

Phase

Connect to the Camera and Acquire Images

Illustration

Description

Connect to a real camera and configure relevant parameters to ensure that the camera can capture images normally.

In the Graphical Programming Workspace of Mech-Vision, select the Capture Images from Camera Step, and click Select camera on the Step Parameters tab.
In the prompted Select camera and calibration parameter group window, click the icon to the right of the camera serial number. When this icon turns into an icon, the camera is connected successfully.

After the camera is connected, click the Select parameter group button and select the calibrated parameter group with ETH/EIH and date.

The calibration parameter group selected here is the one generated after the hand-eye calibration is completed.
After the camera is connected and the parameter group is selected, the calibration parameter group, IP address, and ports of the camera will be obtained automatically. Make sure that Configuration parameter group is set to “Large cartons”.
- Click the Single Step Execution button of the Capture Images from Camera Step to trigger image capturing, double-click the “Camera Depth Map” and “Camera Color Image” data streams of the Step, and check whether the images were successfully captured from the camera in the Debug Output window.

If you can see a normal depth map and color image in the Debug Output window, the Mech-Vision software has successfully connected to the real camera and can capture images normally.

Preprocess Point Clouds and Obtain the Highest-Layer 2D Images

Step name	Preprocess Point Clouds and Obtain the Highest-Layer 2D Images
Phase	Preprocess Point Clouds and Obtain the Highest-Layer 2D Images
Illustration
Description	You need to set an effective recognition region (3D ROI) to keep the interference factors out of the region, and set the “Layer Height” parameter to avoid errors in extracting the 2D image of the highest layer.

Step name

Preprocess Point Clouds and Obtain the Highest-Layer 2D Images

Phase

Preprocess Point Clouds and Obtain the Highest-Layer 2D Images

Illustration

Description

You need to set an effective recognition region (3D ROI) to keep the interference factors out of the region, and set the “Layer Height” parameter to avoid errors in extracting the 2D image of the highest layer.

Set an effective recognition region (3D ROI) to keep the interference factors out of the region to improve recognition efficiency. The set 3D ROI should cover the cartons with an appropriate extension of about 50 mm to accommodate the incoming material deviation.
Set the “Layer Height” parameter for obtaining the highest-layer point cloud. The set parameter value should be smaller than the height of a carton and larger than the maximum height difference of the cartons on the same layer.

Deep Learning Model Package Inference

Step name	Deep Learning Model Package Inference
Phase	Deep Learning Inference
Illustration
Description	Import and load the deep learning model package that is built into the solution and set the 2D ROI to improve deep learning inference.

Step name

Deep Learning Model Package Inference

Phase

Deep Learning Inference

Illustration

Description

Import and load the deep learning model package that is built into the solution and set the 2D ROI to improve deep learning inference.

Use the Deep Learning Model Package Management tool to import the deep learning model package, and select the imported model package.
Set the 2D ROI for deep learning to improve deep learning inference. The set 2D ROI should cover the top-layer cartons with an appropriate margin of 1/3 to accommodate material position fluctuations.

Calculate Carton Poses and Dimensions

Step name	Calculate Carton Poses and Dimensions (custom Procedure)
Phase	Calculate Carton Poses and Dimensions
Illustration
Description	This Step does not require modifying parameter settings.

Step name

Calculate Carton Poses and Dimensions (custom Procedure)

Phase

Calculate Carton Poses and Dimensions

Illustration

Description

This Step does not require modifying parameter settings.

Adjust Poses V2

Step name	Adjust Poses V2
Phase	Adjust Poses
Illustration
Description	Set parameters to transform poses, adjust poses, sort poses, and filter poses.

Step name

Adjust Poses V2

Phase

Adjust Poses

Illustration

Description

Set parameters to transform poses, adjust poses, sort poses, and filter poses.

After obtaining the target object pose, you need to adjust the pose. The processing procedure is as follows.

With the built-in pose adjustment tool in Mech-Vision, you can easily adjust object poses and optimize the picking sequence. You can start parameter adjustment by opening the pose adjustment tool in either of the following ways.

Click the Config Wizard button on the Step block in the Graphical Programming Workspace.
In the Step Parameters tab, click the Config wizard button.

Follow these steps to adjust parameters:

Transform poses: In the Pose adjustment tab, transform poses from the camera reference frame to the robot reference frame.
Adjust poses: In the Pose adjustment tab, set Orientation to “Auto alignment” and select the "Box depalletizing and palletizing" application scenario.
Sort poses: In the Processing rules tab, select the “Sort by Z shape on plane” sorting type, and set the row and column directions of the reference pose.
Filter poses by angle: In the Processing rules tab, filter out poses that are obviously unpickable or are false recognition results according to their Z-axis directions, reducing the time spent on path planning in Mech-Viz.

Output

Step name	Output
Phase	Plan Robot Path (as prerequisites)
Illustration
Description	You need to confirm that the information needed for path planning and collision detection can be output.

Step name

Output

Phase

Plan Robot Path (as prerequisites)

Illustration

Description

You need to confirm that the information needed for path planning and collision detection can be output.

According to the requirements of path planning and collision detection, you need to output the following information to the Mech-Viz software:

Object center poses
Colored scene point cloud
Carton upper surface dimensions

Please make sure that all the above ports have data flow connections, and then click the Run button to confirm that the project can run successfully and output the vision result. You can check whether there are logs with vision results in the Vision tab of Mech-Vision’s log panel.

Path Planning

In this example, Mech-Viz is used to obtain the planned path. When using the Standard Interface communication mode, the Mech-Viz project and the robot side need to cooperate to implement the 3D vision-guided robot picking and placing process.

For details on how to configure the Mech-Viz project for path planning, please refer to the section Picking and Placing.

Now you have completed configuring the vision project.

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