AprilTags and PhotonPoseEstimator

Note

For more information on how to methods to get AprilTag data, look here.

PhotonLib includes a PhotonPoseEstimator class, which allows you to combine the pose data from all tags in view in order to get a field relative pose. For each camera, a separate instance of the PhotonPoseEstimator class should be created.

Creating an AprilTagFieldLayout

AprilTagFieldLayout is used to represent a layout of AprilTags within a space (field, shop at home, classroom, etc.). WPILib provides a JSON that describes the layout of AprilTags on the field which you can then use in the AprilTagFieldLayout constructor. You can also specify a custom layout.

The API documentation can be found in here: Java, C++, and Python.

        public static final AprilTagFieldLayout kTagLayout =
                AprilTagFieldLayout.loadField(AprilTagFields.kDefaultField);

inline const frc::AprilTagFieldLayout kTagLayout{
    frc::AprilTagFieldLayout::LoadField(frc::AprilTagField::kDefaultField)};

            AprilTagFieldLayout.loadField(AprilTagField.kDefaultField),

Defining the Robot to Camera Transform3d

Another necessary argument for creating a PhotonPoseEstimator is the Transform3d representing the robot-relative location and orientation of the camera. A Transform3d contains a Translation3d and a Rotation3d. The Translation3d is created in meters and the Rotation3d is created with radians. For more information on the coordinate system, please see the Coordinate Systems documentation.

        public static final Transform3d kRobotToCam =
                new Transform3d(new Translation3d(0.5, 0.0, 0.5), new Rotation3d(0, 0, 0));

inline const frc::Transform3d kRobotToCam{
    frc::Translation3d{0.5_m, 0.0_m, 0.5_m},
    frc::Rotation3d{0_rad, -30_deg, 0_rad}};

kRobotToCam = wpimath.geometry.Transform3d(
    wpimath.geometry.Translation3d(0.5, 0.0, 0.5),
    wpimath.geometry.Rotation3d.fromDegrees(0.0, -30.0, 0.0),
)

Creating a PhotonPoseEstimator

The PhotonPoseEstimator has a constructor that takes an AprilTagFieldLayout (see above), PoseStrategy, PhotonCamera, and Transform3d. PoseStrategy has nine possible values:

  • MULTI_TAG_PNP_ON_COPROCESSOR

    • Calculates a new robot position estimate by combining all visible tag corners. Recommended for all teams as it will be the most accurate.

    • Must configure the AprilTagFieldLayout properly in the UI, please see here for more information.

  • LOWEST_AMBIGUITY

    • Choose the Pose with the lowest ambiguity.

  • CLOSEST_TO_CAMERA_HEIGHT

    • Choose the Pose which is closest to the camera height.

  • CLOSEST_TO_REFERENCE_POSE

    • Choose the Pose which is closest to the pose from setReferencePose().

  • CLOSEST_TO_LAST_POSE

    • Choose the Pose which is closest to the last pose calculated.

  • AVERAGE_BEST_TARGETS

    • Choose the Pose which is the average of all the poses from each tag.

  • MULTI_TAG_PNP_ON_RIO

    • A slower, older version of MULTI_TAG_PNP_ON_COPROCESSOR, not recommended for use.

  • PNP_DISTANCE_TRIG_SOLVE

    • Use distance data from best visible tag to compute a Pose. This runs on the RoboRIO in order to access the robot’s yaw heading, and MUST have addHeadingData called every frame so heading data is up-to-date. Based on a reference implementation by FRC Team 6328 Mechanical Advantage.

  • CONSTRAINED_SOLVEPNP

    • Solve a constrained version of the Perspective-n-Point problem with the robot’s drivebase flat on the floor. This computation takes place on the RoboRIO, and should not take more than 2ms. This also requires addHeadingData to be called every frame so heading data is up to date. If Multi-Tag PNP is enabled on the coprocessor, it will be used to provide an initial seed to the optimization algorithm – otherwise, the multi-tag fallback strategy will be used as the seed.

        photonEstimator =
                new PhotonPoseEstimator(kTagLayout, PoseStrategy.MULTI_TAG_PNP_ON_COPROCESSOR, kRobotToCam);

  photon::PhotonPoseEstimator photonEstimator{
      constants::Vision::kTagLayout,
      photon::PoseStrategy::MULTI_TAG_PNP_ON_COPROCESSOR,
      constants::Vision::kRobotToCam};

        self.camPoseEst = PhotonPoseEstimator(
            AprilTagFieldLayout.loadField(AprilTagField.kDefaultField),
            PoseStrategy.LOWEST_AMBIGUITY,
            self.cam,
            kRobotToCam,
        )

Note

Python still takes a PhotonCamera in the constructor, so you must create the camera as shown in the next section and then return and use it to create the PhotonPoseEstimator.

Using a PhotonPoseEstimator

The final prerequisite to using your PhotonPoseEstimator is creating a PhotonCamera. To do this, you must set the name of your camera in Photon Client. From there you can define the camera in code.

        camera = new PhotonCamera(kCameraName);

  photon::PhotonCamera camera{constants::Vision::kCameraName};

        self.cam = PhotonCamera("YOUR CAMERA NAME")

Calling update() on your PhotonPoseEstimator will return an EstimatedRobotPose, which includes a Pose3d of the latest estimated pose (using the selected strategy) along with a double of the timestamp when the robot pose was estimated.

        Optional<EstimatedRobotPose> visionEst = Optional.empty();
        for (var change : camera.getAllUnreadResults()) {
            visionEst = photonEstimator.update(change);
            updateEstimationStdDevs(visionEst, change.getTargets());

            if (Robot.isSimulation()) {
                visionEst.ifPresentOrElse(
                        est ->
                                getSimDebugField()
                                        .getObject("VisionEstimation")
                                        .setPose(est.estimatedPose.toPose2d()),
                        () -> {
                            getSimDebugField().getObject("VisionEstimation").setPoses();
                        });
            }

            visionEst.ifPresent(
                    est -> {
                        // Change our trust in the measurement based on the tags we can see
                        var estStdDevs = getEstimationStdDevs();

                        estConsumer.accept(est.estimatedPose.toPose2d(), est.timestampSeconds, estStdDevs);
                    });
        }

    // Run each new pipeline result through our pose estimator
    for (const auto& result : camera.GetAllUnreadResults()) {
      // cache result and update pose estimator
      auto visionEst = photonEstimator.Update(result);
      m_latestResult = result;

      // In sim only, add our vision estimate to the sim debug field
      if (frc::RobotBase::IsSimulation()) {
        if (visionEst) {
          GetSimDebugField()
              .GetObject("VisionEstimation")
              ->SetPose(visionEst->estimatedPose.ToPose2d());
        } else {
          GetSimDebugField().GetObject("VisionEstimation")->SetPoses({});
        }
      }

      if (visionEst) {
        estConsumer(visionEst->estimatedPose.ToPose2d(), visionEst->timestamp,
                    GetEstimationStdDevs(visionEst->estimatedPose.ToPose2d()));
      }

        camEstPose = self.camPoseEst.update()

You should be updating your drivetrain pose estimator with the result from the PhotonPoseEstimator every loop using addVisionMeasurement().

        vision = new Vision(drivetrain::addVisionMeasurement);

  Vision vision{[=, this](frc::Pose2d pose, units::second_t timestamp,
                          Eigen::Matrix<double, 3, 1> stddevs) {
    drivetrain.AddVisionMeasurement(pose, timestamp, stddevs);
  }};

        if camEstPose:
            self.swerve.addVisionPoseEstimate(
                camEstPose.estimatedPose, camEstPose.timestampSeconds
            )

Complete Examples

The complete examples for the PhotonPoseEstimator can be found in the following locations:

Additional PhotonPoseEstimator Methods

For more information on the PhotonPoseEstimator class, please see the API documentation.