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Configure an obstacles_distance Segmenter

Changed in RDK v0.2.36 and API v0.1.118

obstacles_distance is a segmenter that takes point clouds from a camera input and returns the average single closest point to the camera as a perceived obstacle. It is best for transient obstacle avoidance.

For example, if you have an ultrasonic distance sensor as viam:ultrasonic:camera, this model will query the sensor "num_queries" times, and then take the average point from those measurements and return that as an obstacle.

First, make sure your camera is connected to your machine’s computer and both are powered on. Then, configure the service:

Navigate to the CONFIGURE tab of your machine’s page in the Viam app. Click the + icon next to your machine part in the left-hand menu and select Service. Select the vision type, then select the obstacles distance model. Enter a name or use the suggested name for your service and click Create.

In your vision service’s configuration panel, fill in the attributes field with the following:

{
  "num_queries": 10
}

Add the vision service object to the services array in your raw JSON configuration:

"services": [
    {
      "name": "<segmenter_name>",
      "api": "rdk:service:vision",
      "model": "obstacles_distance",
      "attributes": {
        "num_queries": 10
      }
    },
    ... // Other services
]

"services": [
    {
        "name": "my_segmenter",
        "api": "rdk:service:vision",
        "model": "obstacles_distance",
        "attributes": {
            "num_queries": 10
        }
    }
]

The following parameters are available for a obstacles_distance segmenter:

Parameter	Required?	Description
`num_queries`	Optional	How many times the model should call `GetPointCloud()` before taking the average of the measurements and returning the single closest point. Accepts an integer between `1` and `20`. Default: `10`

Test your segmenter

The following code uses the GetObjectPointClouds method to run a segmenter vision model on an image from the machine’s camera "cam1":

Python
Go

from viam.services.vision import VisionClient

robot = await connect()

# Grab Viam's vision service for the segmenter
my_segmenter = VisionClient.from_robot(robot, "my_segmenter")

objects = await my_segmenter.get_object_point_clouds("cam1")

await robot.close()

To learn more about how to use segmentation, see the Python SDK docs.

import (
"go.viam.com/rdk/config"
"go.viam.com/rdk/services/vision"
"go.viam.com/rdk/components/camera"
)

cameraName := "cam1" // Use the same component name that you have in your machine configuration

// Get the vision service you configured with name "my_segmenter" from the machine
mySegmenter, err := vision.from_robot(robot, "my_segmenter")
if err != nil {
    logger.Fatalf("Cannot get vision service: %v", err)
}

// Get segments
segments, err := mySegmenter.ObjectPointClouds(context.Background(), cameraName, nil)
if err != nil {
    logger.Fatalf("Could not get segments: %v", err)
}
if len(segments) > 0 {
    logger.Info(segments[0])
}

To learn more about how to use segmentation, see the Go SDK docs.

Tip

To see more code examples of how to use Viam’s vision service, see our example repo.

Next Steps

For general configuration and development info, see:

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