You can now build custom AprilTag maps with an intuitive UI.
The default family and tag size have been updated to match the 2024 field.
New Hardware Manager
The Finder Tool is now the Limelight Hardware Manager
It has been rewritten from scratch. It now reliably detects Limelights, provides more useful diagnostic information, and does not require restarts to work properly.
The map and detector model have been added to the downloads page and the latest Limelight OS image.
Limelight OS 2024.0 (2/6/24)
ChArUco Calibration Fixes
Our ChArUco detector’s subpixel accuracy has been increased. A reprojection error of 1-2 pixels is now achievable with clipboard targets and 20 images.
Using the same camera and the same target, 2023.6 achieved an RPE of > 20 pixels, and 2024.0 achieved an RPE of 1.14 pixels.
Input fields no longer accept letters and special characters. This eliminates the potential for a crash.
Out-Of-The-Box Megatag Accuracy Improvement
Before this update, Limelight’s internal Megatag map generator referenced the UI’s tag size slider instead of the tag sizes supplied by the .fmap file.
Megatag now respects the tag sizes configured in fmap files and ignores the size slider.
If your size slider has not been set to 165.1 mm, you will notice an immediate improvement in localization accuracy
Performance Upgrades and Bugfixes
Higher FPS AprilTag pipelines
The performance of the Field-Space Visualizer has been significantly improved.
Bugfixes
Apriltags in 3D visualizers were sometimes drawn with incorrect or corrupted tag images. Tags are now always displayed correctly.
“v” / tv / “valid” will now only return “1” if there are valid detections. Previously, tv was always “1”
A few notes
You can score notes in the speaker by looking at the centered speaker apriltag, using crosshair calibration, and using “tx” as an input into a proportional control loop.
e.g.
swerveTurnSpeed = tx * .005
If you need to shoot from several locations, first use the apriltag 3d point offset feature, crosshair calibration, and tx before you attempt to use your robot’s pose to aim.
You can also perform automated pickup, climbing, and amp scoring with simple 2D pipelines. You do not need to implement real-time path generation to play this year’s game, and you can build a competitive robot with a simple approach to vision.
I mean, really? It took me like 20 seconds to find another one
Come on, this is the second time you’ve claimed “first ever” something and I’ve had to call you out on it. Don’t pull a Vex 2020 and let marketing garbage tear down an otherwise good reputation.
Personally, I’d either use the FIRST provided tags or throw some python together in a colab notebook. I’ll double check it later but I generally trust matplotlib to do sane things. Took me 2 minutes to generate april tags there that at first glance, appear accurate in size (look, my printer is perennially out of ink it’s a whole thing)
Just gave off “it’s the only tag generator you’ll ever need” vibes and rubbed me the wrong way.
I noticed that there wasn’t any mention of upgrading to NT4. Are there plans to upgrade to NT4 and possibly utilize structs or protobuf instead of sending over a giant json string? Being able to use these in LimelightLib instead of having to parse the json would be much faster. This is especially beneficial to teams using multiple Limelights, where just parsing alone can take up 5-7 ms every loop run.
@jdaming
The gif shown in the neural network section is using the built-in neural network pipeline. This model was built with the new Colab tutorial to make sure everything works as intended
If you’re referring to note-tracking, you can use TX and TY to servo on the note with your drivetrain. The 2D crosshair calibration and sorting features are usually necessary while tracking game pieces.
Check this link. You can use this point of interest offset and standard tx/ty tracking to always track the exact center of the goal if you need a wide range of shooting locations
@Brandon_Hjelstrom Thanks for getting this update out and supporting the community. We still love this product. Best hardware and software around for FRC.
Can you update us on when a new batch of LL3s will be available? Andymark shows mid-Feb. Does that sounds right?
I’m software-dumb, so forgive me - We have a LL2+, will we be able to use the 2024 updates that don’t involve the specific hardware of the LL3 (assuming no neural networks, no google coral, etc).
Is it possible to run MegaTag and the Note Detector off a single limelight? Our team only has one LL2+ but I would still like to have both field positioning and the ability to automatically drive to and pick up a note. Thank you in advance!
You can create two pipelines, one which tracks notes and a second that tracks april tags. Then you can switch between the two pipelines on the fly depending on what you’re trying to do.
Have you tried swapping Jackson to another library such as FastJSON? Jackson, while extremely popular and well supported, is not known for its speed*.
It’s likely worth running this benchmark program on a Rio. Swapping LimelightHelper’s parsing logic is quite trivial for anyone who is this far in the weeds already.
In any case, I’m very much looking forward to not having to parse JSON anymore just to count the number of targets we see.
(*Not that it’s insanely slow, just not the absolute fastest.)
We are having trouble installing the update. On balena etcher, after we select the zip file, it won’t let us select “compute module” - it says "missing drivers.