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. The PhotonPoseEstimator class works with one camera per object instance, but more than one instance may 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 and C++.

// The field from AprilTagFields will be different depending on the game.
AprilTagFieldLayout aprilTagFieldLayout = AprilTagFieldLayout.loadField(AprilTagFields.kDefaultField);

// The parameter for LoadAPrilTagLayoutField will be different depending on the game.
frc::AprilTagFieldLayout aprilTagFieldLayout = frc::LoadAprilTagLayoutField(frc::AprilTagField::kDefaultField);

# Coming Soon!

Creating a PhotonPoseEstimator

The PhotonPoseEstimator has a constructor that takes an AprilTagFieldLayout (see above), PoseStrategy, PhotonCamera, and Transform3d. PoseStrategy has six 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.

//Forward Camera
cam = new PhotonCamera("testCamera");
Transform3d robotToCam = new Transform3d(new Translation3d(0.5, 0.0, 0.5), new Rotation3d(0,0,0)); //Cam mounted facing forward, half a meter forward of center, half a meter up from center.

// Construct PhotonPoseEstimator
PhotonPoseEstimator photonPoseEstimator = new PhotonPoseEstimator(aprilTagFieldLayout, PoseStrategy.CLOSEST_TO_REFERENCE_POSE, cam, robotToCam);

// Forward Camera
std::shared_ptr<photonlib::PhotonCamera> cameraOne =
    std::make_shared<photonlib::PhotonCamera>("testCamera");
// Camera is mounted facing forward, half a meter forward of center, half a
// meter up from center.
frc::Transform3d robotToCam =
    frc::Transform3d(frc::Translation3d(0.5_m, 0_m, 0.5_m),
                    frc::Rotation3d(0_rad, 0_rad, 0_rad));

// ... Add other cameras here

// Assemble the list of cameras & mount locations
std::vector<
    std::pair<std::shared_ptr<photonlib::PhotonCamera>, frc::Transform3d>>
    cameras;
cameras.push_back(std::make_pair(cameraOne, robotToCam));

photonlib::RobotPoseEstimator estimator(
    aprilTags, photonlib::CLOSEST_TO_REFERENCE_POSE, cameras);

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

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

self.camPoseEst = PhotonPoseEstimator(
    loadAprilTagLayoutField(AprilTagField.kDefaultField),
    PoseStrategy.CLOSEST_TO_REFERENCE_POSE,
    self.cam,
    kRobotToCam
)

Using a PhotonPoseEstimator

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

    public Optional<EstimatedRobotPose> getEstimatedGlobalPose(Pose2d prevEstimatedRobotPose) {
        photonPoseEstimator.setReferencePose(prevEstimatedRobotPose);
        return photonPoseEstimator.update();
    }

std::pair<frc::Pose2d, units::millisecond_t> getEstimatedGlobalPose(
    frc::Pose3d prevEstimatedRobotPose) {
  robotPoseEstimator.SetReferencePose(prevEstimatedRobotPose);
  units::millisecond_t currentTime = frc::Timer::GetFPGATimestamp();
  auto result = robotPoseEstimator.Update();
  if (result.second) {
    return std::make_pair<>(result.first.ToPose2d(),
                            currentTime - result.second);
  } else {
    return std::make_pair(frc::Pose2d(), 0_ms);
  }
}

# Coming Soon!

You should be updating your drivetrain pose estimator with the result from the RobotPoseEstimator every loop using addVisionMeasurement(). TODO: add example note

Additional PhotonPoseEstimator Methods

setReferencePose(Pose3d referencePose)

Updates the stored reference pose when using the CLOSEST_TO_REFERENCE_POSE strategy.

setLastPose(Pose3d lastPose)

Update the stored last pose. Useful for setting the initial estimate when using the CLOSEST_TO_LAST_POSE strategy.