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 an 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.

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>",
      "type": "vision",
      "namespace": "rdk",
      "model": "obstacles_distance",
      "attributes": {
        "num_queries": 10
      }
    },
    ... // Other services
]

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

The following parameters are available for a obstacles_distance segmenter:

ParameterInclusionDescription
num_queriesOptionalHow 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":

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.

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If you notice any issues with the documentation, feel free to file an issue or edit this file.