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What is a Limelight?

A Limelight is a small, on-board computer and camera system that performs computer vision. Many FRC teams, including us, use a Limelight assisting the driver in multiple tasks such as auto aiming and auto movement. Limelights are expensive and should be handled carefully.

Take a look at the instructions here for setting up the Limelight. As well, you should read how to build and configure a vision pipeline here.

Generally, the Limelight draws a bounding box around the target and then obtains values from this rectangle.

There are two main pipelines ("modes") we use are: AprilTags and Neural Networks.


Apriltag in the wild

Apriltags are essentially QR codes. These are placed throughout the field, such as on scoring targets. Since they have set locations on the field, it is useful for us to use these to detect what things we can do in our location (for example, if we can see that we are in front of a goal then we can shoot).

On the other hand, we use the Neural Network pipeline to detect custom objects. Usually, we train a neural network to find these objects, and then we can get measurement values from it. We'll go more in depth later.

There are two important interfaces for the limelight: http://limelight.local:5801 and http://limelight.local:5800 (you need to be connected to the limelight via the radio to use these links). The first one is for configuring the Limelight pipeline and the second one is for displaying the camera feed.

Limelight NetworkTables values

NetworkTables might seem like a new concept, but you have already been working with NetworkTables because it includes SmartDashboard as one of its keys. Whenever you put or read values using SmartDashboard, you were working with NetworkTables. You can think of every instance of SmartDashboard as NetworkTableInstance.getDefault().getTable("SmartDashboard"). You can find a full description of NetworkTables here,

Most important for this section is the fact that the Limelight puts many useful values to NetworkTables. You can access those values by specifying a key by writing:


Important keys that you will use often includes but not limited to: tv, tx, ty, ta, and etc. You may want to store the Limelight NetworkTable as a variable depending on how often you plan on accessing its entries. You can find a full list of keys here.


Instead of using this long mess every time, consider copy-pasting the LimelightHelpers class into the subsystem file and using its methods

Distance Estimation and Angle Alignment

Here's some example code. There are more examples in the Limelight documentation.

First, finding the ground distance from the limelight to a target:

public double getDistance() {
    Rotation2d angleToGoal = Rotation2d.fromDegrees(LIMELIGHT_MOUNT_ANGLE)
    double distance = (TARGET_HEIGHT - LIMELIGHT_HEIGHT) / angleToGoal.getTan();
    return distance;

Let's predefine some constants: LIMELIGHT_MOUNT_ANGLE - the angle that the limelight is mounted from TARGET_HEIGHT - the height of the target LIMELIGHT_HEIGHT - the height of the limelight from the ground

This code uses basic trigonometry to calculate the floor distance. This method can be used to move to some location.

We can also align to something with Limelight:

public double getRotation() {
    double cameraLensHorizontalOffset = LimelightHelpers.getTX("limelight") / getDistance();
    double realHorizontalOffset = Math.atan(cameraLensHorizontalOffset / getDistance());
    double rotationError = Math.atan(realHorizontalOffset / getDistance());
    return rotationError;

This returns the angle (in radians!) that the drivetrain must turn to align to its target. It also uses basic trigonometry.


Code with Limelight often involves a lot of math, especially trig. If that's not your cup of tea, try the tea again :P

Now take a look at from RobotCode2020. In particular, take a look at distanceAssist(), steeringAssist(), and autoTarget(). Some things I will point about about the code, especially steeringAssist(), are:

  1. We use the PIDController class which allows us the ability to use kP as well as kI and kD if we wish.

  2. We use ta to determine if the bounding box is large enough to consider as a vision target.

  3. When steeringAssist() does not see a bounding box, it turns in the direction of the last tx value. This is to make sure that if it overshoots or if the target changes direction, it will take the shortest path to try to face the target again.

The programming team had some fun with this code during the 2019-2020 Pre-Season:

In 2024, we used Limelight to autoalign then intake the game piece. Check out this code to see how we did it!

If it's too long to read, basically what it does is: 1. Change the drivetrain to robot oriented and turn on the intake 2. Retrieves three offsets: forward, strafe, and angle 3. It ends when there is a game piece inside the robot 4. The drivetrain is back to field oriented so it is driveable for the driver and the intake is stopped

Now go back and read it again :)

What if I want to detect other things?

Well now you're at the right section. Using Limelight, we can detect a bunch of different objects, like game pieces and people!

There are two (main) types of vision models: classifier and detector.

A classifier is used to categorize an entire image into a predefined label. For example, if you want to distinguish between a red ball and a blue ball, you would use a classifier.

A detector is used to find specific distances within an image. This is usually used more often. For example, if you want to find the bounding box and specific location of that ball, you would use a detector.

For more details on training a model, check this out. There are also several pretrained models online you can steal >:)

Once you have your files, change the pipeline option to "Neural Network" and upload the files to the Limelight interface. Then watch the magic happen 😼😼

Some general notes to keep in mind when coding: - Make sure you keep stay consistent with units - Remember to store the name of your limelight(s) in constants - In commands, don't make Limelight a requirement so that it can be used by multiple commands at the same time