mirror of
https://github.com/PhotonVision/photonvision
synced 2026-06-19 00:41:41 +00:00
e970446c4c
We added new API methods in 2026, and deprecated our past methods. This PR removes the deprecated methods.
767 lines
33 KiB
Java
767 lines
33 KiB
Java
/*
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* MIT License
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*
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* Copyright (c) PhotonVision
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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package org.photonvision;
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import java.util.*;
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import org.photonvision.estimation.TargetModel;
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import org.photonvision.estimation.VisionEstimation;
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import org.photonvision.targeting.PhotonPipelineResult;
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import org.photonvision.targeting.PhotonTrackedTarget;
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import org.wpilib.driverstation.DriverStation;
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import org.wpilib.hardware.hal.HAL;
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import org.wpilib.math.geometry.Pose2d;
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import org.wpilib.math.geometry.Pose3d;
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import org.wpilib.math.geometry.Rotation2d;
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import org.wpilib.math.geometry.Rotation3d;
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import org.wpilib.math.geometry.Transform3d;
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import org.wpilib.math.geometry.Translation2d;
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import org.wpilib.math.geometry.Translation3d;
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import org.wpilib.math.interpolation.TimeInterpolatableBuffer;
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import org.wpilib.math.linalg.Matrix;
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import org.wpilib.math.numbers.N1;
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import org.wpilib.math.numbers.N3;
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import org.wpilib.math.numbers.N8;
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import org.wpilib.math.util.Pair;
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import org.wpilib.vision.apriltag.AprilTagFieldLayout;
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/**
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* The PhotonPoseEstimator class filters or combines readings from all the AprilTags visible at a
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* given timestamp on the field to produce a single robot in field pose, using the strategy set
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* below. Example usage can be found in our apriltagExample example project.
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*/
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public class PhotonPoseEstimator {
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private static int InstanceCount = 1;
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/** Position estimation strategies that can be used by the {@link PhotonPoseEstimator} class. */
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public enum PoseStrategy {
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/** Choose the Pose with the lowest ambiguity. */
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LOWEST_AMBIGUITY,
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/** Choose the Pose which is closest to the camera height. */
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CLOSEST_TO_CAMERA_HEIGHT,
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/** Choose the Pose which is closest to a set Reference position. */
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CLOSEST_TO_REFERENCE_POSE,
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/** Choose the Pose which is closest to the last pose calculated */
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CLOSEST_TO_LAST_POSE,
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/** Return the average of the best target poses using ambiguity as weight. */
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AVERAGE_BEST_TARGETS,
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/**
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* Use all visible tags to compute a single pose estimate on coprocessor. This option needs to
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* be enabled on the PhotonVision web UI as well.
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*/
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MULTI_TAG_PNP_ON_COPROCESSOR,
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/**
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* Use all visible tags to compute a single pose estimate. This runs on the RoboRIO, and can
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* take a lot of time.
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*/
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MULTI_TAG_PNP_ON_RIO,
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/**
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* Use distance data from best visible tag to compute a Pose. This runs on the RoboRIO in order
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* to access the robot's yaw heading, and MUST have addHeadingData called every frame so heading
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* data is up-to-date.
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*
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* <p>Yields a Pose2d in estimatedRobotPose (0 for z, roll, pitch)
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*
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* <p>https://www.chiefdelphi.com/t/frc-6328-mechanical-advantage-2025-build-thread/477314/98
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*/
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PNP_DISTANCE_TRIG_SOLVE,
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/**
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* Solve a constrained version of the Perspective-n-Point problem with the robot's drivebase
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* flat on the floor. This computation takes place on the RoboRIO, and typically takes not more
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* than 2ms. See {@link PhotonPoseEstimator.ConstrainedSolvepnpParams} and {@link
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* org.photonvision.jni.ConstrainedSolvepnpJni} for details and tuning handles this strategy
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* exposes. This strategy needs addHeadingData called every frame so heading data is up-to-date.
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* If Multi-Tag PNP is enabled on the coprocessor, it will be used to provide an initial seed to
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* the optimization algorithm -- otherwise, the multi-tag fallback strategy will be used as the
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* seed.
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*/
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CONSTRAINED_SOLVEPNP
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}
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/**
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* Tuning handles we have over the CONSTRAINED_SOLVEPNP {@link PhotonPoseEstimator.PoseStrategy}.
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* Internally, the cost function is a sum-squared of pixel reprojection error + (optionally)
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* heading error * heading scale factor.
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*
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* @param headingFree If true, heading is completely free to vary. If false, heading excursions
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* from the provided heading measurement will be penalized
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* @param headingScaleFactor If headingFree is false, this weights the cost of changing our robot
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* heading estimate against the tag corner reprojection error cost.
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*/
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public static final record ConstrainedSolvepnpParams(
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boolean headingFree, double headingScaleFactor) {}
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private AprilTagFieldLayout fieldTags;
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private TargetModel tagModel = TargetModel.kAprilTag36h11;
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private Transform3d robotToCamera;
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private final Set<Integer> reportedErrors = new HashSet<>();
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private final TimeInterpolatableBuffer<Rotation2d> headingBuffer =
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TimeInterpolatableBuffer.createBuffer(1.0);
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/**
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* Create a new PhotonPoseEstimator.
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*
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* @param fieldTags A WPILib {@link AprilTagFieldLayout} linking AprilTag IDs to Pose3d objects
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* with respect to the FIRST field using the <a href=
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* "https://docs.wpilib.org/en/stable/docs/software/advanced-controls/geometry/coordinate-systems.html#field-coordinate-system">Field
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* Coordinate System</a>. Note that setting the origin of this layout object will affect the
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* results from this class.
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* @param robotToCamera Transform3d from the center of the robot to the camera mount position (ie,
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* robot ➔ camera) in the <a href=
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* "https://docs.wpilib.org/en/stable/docs/software/advanced-controls/geometry/coordinate-systems.html#robot-coordinate-system">Robot
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* Coordinate System</a>.
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*/
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public PhotonPoseEstimator(AprilTagFieldLayout fieldTags, Transform3d robotToCamera) {
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this.fieldTags = fieldTags;
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this.robotToCamera = robotToCamera;
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HAL.reportUsage("PhotonVision/PhotonPoseEstimator", InstanceCount, "");
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InstanceCount++;
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}
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/**
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* Get the AprilTagFieldLayout being used by the PositionEstimator.
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*
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* <p>Note: Setting the origin of this layout will affect the results from this class.
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*
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* @return the AprilTagFieldLayout
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*/
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public AprilTagFieldLayout getFieldTags() {
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return fieldTags;
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}
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/**
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* Set the AprilTagFieldLayout being used by the PositionEstimator.
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*
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* <p>Note: Setting the origin of this layout will affect the results from this class.
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*
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* @param fieldTags the AprilTagFieldLayout
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*/
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public void setFieldTags(AprilTagFieldLayout fieldTags) {
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this.fieldTags = fieldTags;
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}
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/**
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* Get the TargetModel representing the tags being detected. This is used for on-rio multitag.
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*
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* <p>By default, this is {@link TargetModel#kAprilTag36h11}.
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*/
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public TargetModel getTagModel() {
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return tagModel;
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}
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/**
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* Set the TargetModel representing the tags being detected. This is used for on-rio multitag.
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*
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* @param tagModel E.g. {@link TargetModel#kAprilTag16h5}.
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*/
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public void setTagModel(TargetModel tagModel) {
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this.tagModel = tagModel;
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}
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/**
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* Add robot heading data to buffer. Must be called periodically for the
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* <b>PNP_DISTANCE_TRIG_SOLVE</b> strategy.
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*
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* @param timestampSeconds Timestamp of the robot heading data.
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* @param heading Field-relative robot heading at given timestamp. Standard WPILIB field
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* coordinates.
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*/
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public void addHeadingData(double timestampSeconds, Rotation3d heading) {
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addHeadingData(timestampSeconds, heading.toRotation2d());
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}
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/**
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* Add robot heading data to buffer. Must be called periodically for the
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* <b>PNP_DISTANCE_TRIG_SOLVE</b> strategy.
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*
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* @param timestampSeconds Timestamp of the robot heading data.
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* @param heading Field-relative robot heading at given timestamp. Standard WPILIB field
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* coordinates.
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*/
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public void addHeadingData(double timestampSeconds, Rotation2d heading) {
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headingBuffer.addSample(timestampSeconds, heading);
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}
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/**
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* Clears all heading data in the buffer, and adds a new seed. Useful for preventing estimates
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* from utilizing heading data provided prior to a pose or rotation reset.
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*
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* @param timestampSeconds Timestamp of the robot heading data.
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* @param heading Field-relative robot heading at given timestamp. Standard WPILIB field
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* coordinates.
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*/
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public void resetHeadingData(double timestampSeconds, Rotation3d heading) {
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headingBuffer.clear();
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addHeadingData(timestampSeconds, heading);
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}
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/**
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* Clears all heading data in the buffer, and adds a new seed. Useful for preventing estimates
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* from utilizing heading data provided prior to a pose or rotation reset.
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*
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* @param timestampSeconds Timestamp of the robot heading data.
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* @param heading Field-relative robot heading at given timestamp. Standard WPILIB field
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* coordinates.
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*/
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public void resetHeadingData(double timestampSeconds, Rotation2d heading) {
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headingBuffer.clear();
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addHeadingData(timestampSeconds, heading);
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}
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/**
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* @return The current transform from the center of the robot to the camera mount position
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*/
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public Transform3d getRobotToCameraTransform() {
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return robotToCamera;
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}
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/**
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* Useful for pan and tilt mechanisms and such.
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*
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* @param robotToCamera The current transform from the center of the robot to the camera mount
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* position
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*/
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public void setRobotToCameraTransform(Transform3d robotToCamera) {
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this.robotToCamera = robotToCamera;
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}
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/**
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* @param cameraResult A pipeline result from the camera.
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* @return Whether or not pose estimation should be performed.
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*/
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private boolean shouldEstimate(PhotonPipelineResult cameraResult) {
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// Time in the past -- give up, since the following if expects times > 0
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if (cameraResult.getTimestampSeconds() < 0) {
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return false;
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}
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// If no targets seen, trivial case -- can't do estimation
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return cameraResult.hasTargets();
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}
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/**
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* Return the estimated position of the robot by using distance data from best visible tag to
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* compute a Pose. This runs on the RoboRIO in order to access the robot's yaw heading, and MUST
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* have addHeadingData called every frame so heading data is up-to-date.
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*
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* <p>Yields a Pose2d in estimatedRobotPose (0 for z, roll, pitch)
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*
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* <p>https://www.chiefdelphi.com/t/frc-6328-mechanical-advantage-2025-build-thread/477314/98
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*
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* @param cameraResult A pipeline result from the camera.
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* @return An {@link EstimatedRobotPose} with an estimated pose, timestamp, and targets used to
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* create the estimate, or an empty optional if there's no targets or heading data.
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*/
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public Optional<EstimatedRobotPose> estimatePnpDistanceTrigSolvePose(
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PhotonPipelineResult cameraResult) {
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if (!shouldEstimate(cameraResult)) {
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return Optional.empty();
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}
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PhotonTrackedTarget bestTarget = cameraResult.getBestTarget();
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if (bestTarget == null) return Optional.empty();
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var headingSampleOpt = headingBuffer.getSample(cameraResult.getTimestampSeconds());
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if (headingSampleOpt.isEmpty()) {
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return Optional.empty();
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}
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Rotation2d headingSample = headingSampleOpt.get();
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Translation2d camToTagTranslation =
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new Translation3d(
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bestTarget.getBestCameraToTarget().getTranslation().getNorm(),
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new Rotation3d(
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0,
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-Math.toRadians(bestTarget.getPitch()),
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-Math.toRadians(bestTarget.getYaw())))
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.rotateBy(robotToCamera.getRotation())
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.toTranslation2d()
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.rotateBy(headingSample);
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var tagPoseOpt = fieldTags.getTagPose(bestTarget.getFiducialId());
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if (tagPoseOpt.isEmpty()) {
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return Optional.empty();
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}
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var tagPose2d = tagPoseOpt.get().toPose2d();
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Translation2d fieldToCameraTranslation =
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tagPose2d.getTranslation().plus(camToTagTranslation.unaryMinus());
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Translation2d camToRobotTranslation =
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robotToCamera.getTranslation().toTranslation2d().unaryMinus().rotateBy(headingSample);
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Pose2d robotPose =
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new Pose2d(fieldToCameraTranslation.plus(camToRobotTranslation), headingSample);
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return Optional.of(
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new EstimatedRobotPose(
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new Pose3d(robotPose),
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cameraResult.getTimestampSeconds(),
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cameraResult.getTargets(),
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PoseStrategy.PNP_DISTANCE_TRIG_SOLVE));
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}
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/**
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* Return the estimated position of the robot by solving a constrained version of the
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* Perspective-n-Point problem with the robot's drivebase flat on the floor. This computation
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* takes place on the RoboRIO, and typically takes not more than 2ms. See {@link
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* org.photonvision.jni.ConstrainedSolvepnpJni} for tuning handles this strategy exposes.
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* Internally, the cost function is a sum-squared of pixel reprojection error + (optionally)
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* heading error * heading scale factor. This strategy needs addHeadingData called every frame so
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* heading data is up-to-date.
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*
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* @param cameraResult A pipeline result from the camera.
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* @param cameraMatrix Camera intrinsics from camera calibration data.
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* @param distCoeffs Distortion coefficients from camera calibration data.
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* @param seedPose An initial guess at robot pose, refined via optimization. Better guesses will
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* converge faster. Can come from any pose source, but some battle-tested sources are {@link
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* #estimateCoprocMultiTagPose(PhotonPipelineResult)}, or {@link
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* #estimateLowestAmbiguityPose(PhotonPipelineResult)} if MultiTag results aren't available.
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* @param headingFree If true, heading is completely free to vary. If false, heading excursions
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* from the provided heading measurement will be penalized
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* @param headingScaleFactor If headingFree is false, this weights the cost of changing our robot
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* heading estimate against the tag corner reprojection error cont.
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* @return An {@link EstimatedRobotPose} with an estimated pose, timestamp, and targets used to
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* create the estimate, or an empty optional if there's no targets or heading data, or if the
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* solver fails to solve the problem.
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*/
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public Optional<EstimatedRobotPose> estimateConstrainedSolvepnpPose(
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PhotonPipelineResult cameraResult,
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Matrix<N3, N3> cameraMatrix,
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Matrix<N8, N1> distCoeffs,
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Pose3d seedPose,
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boolean headingFree,
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double headingScaleFactor) {
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if (!shouldEstimate(cameraResult)) {
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return Optional.empty();
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}
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// Need heading if heading fixed
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if (!headingFree) {
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if (headingBuffer.getSample(cameraResult.getTimestampSeconds()).isEmpty()) {
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return Optional.empty();
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} else {
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// If heading fixed, force rotation component
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seedPose =
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new Pose3d(
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seedPose.getTranslation(),
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new Rotation3d(headingBuffer.getSample(cameraResult.getTimestampSeconds()).get()));
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}
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}
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var pnpResult =
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VisionEstimation.estimateRobotPoseConstrainedSolvepnp(
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cameraMatrix,
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distCoeffs,
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cameraResult.getTargets(),
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robotToCamera,
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seedPose,
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fieldTags,
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tagModel,
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headingFree,
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headingBuffer.getSample(cameraResult.getTimestampSeconds()).get(),
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headingScaleFactor);
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if (!pnpResult.isPresent()) return Optional.empty();
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var best = Pose3d.kZero.plus(pnpResult.get().best); // field-to-robot
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return Optional.of(
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new EstimatedRobotPose(
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best,
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cameraResult.getTimestampSeconds(),
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cameraResult.getTargets(),
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PoseStrategy.CONSTRAINED_SOLVEPNP));
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}
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/**
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* Return the estimated position of the robot by using all visible tags to compute a single pose
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* estimate on coprocessor. This option needs to be enabled on the PhotonVision web UI as well.
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*
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* @param cameraResult A pipeline result from the camera.
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* @return An {@link EstimatedRobotPose} with an estimated pose, timestamp, and targets used to
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* create the estimate, or an empty optional if there's no targets, no multi-tag results, or
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* multi-tag is disabled in the web UI.
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*/
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public Optional<EstimatedRobotPose> estimateCoprocMultiTagPose(
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PhotonPipelineResult cameraResult) {
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if (cameraResult.getMultiTagResult().isEmpty() || !shouldEstimate(cameraResult)) {
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return Optional.empty();
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}
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var best_tf = cameraResult.getMultiTagResult().get().estimatedPose.best;
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var best =
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Pose3d.kZero
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.plus(best_tf) // field-to-camera
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.relativeTo(fieldTags.getOrigin())
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.plus(robotToCamera.inverse()); // field-to-robot
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return Optional.of(
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new EstimatedRobotPose(
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best,
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cameraResult.getTimestampSeconds(),
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cameraResult.getTargets(),
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PoseStrategy.MULTI_TAG_PNP_ON_COPROCESSOR));
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}
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/**
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* Return the estimated position of the robot by using all visible tags to compute a single pose
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* estimate on the RoboRIO. This can take a lot of time due to the RIO's weak computing power.
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*
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* @param cameraResult A pipeline result from the camera.
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* @param cameraMatrix Camera intrinsics from camera calibration data
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* @param distCoeffs Distortion coefficients from camera calibration data.
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* @return An {@link EstimatedRobotPose} with an estimated pose, timestamp, and targets used to
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* create the estimate, or an empty optional if there's less than 2 targets visible or
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* SolvePnP fails.
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*/
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public Optional<EstimatedRobotPose> estimateRioMultiTagPose(
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PhotonPipelineResult cameraResult, Matrix<N3, N3> cameraMatrix, Matrix<N8, N1> distCoeffs) {
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if (cameraResult.getTargets().size() < 2 || !shouldEstimate(cameraResult)) {
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return Optional.empty();
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}
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var pnpResult =
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VisionEstimation.estimateCamPosePNP(
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cameraMatrix, distCoeffs, cameraResult.getTargets(), fieldTags, tagModel);
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if (!pnpResult.isPresent()) return Optional.empty();
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var best =
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Pose3d.kZero
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.plus(pnpResult.get().best) // field-to-camera
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.plus(robotToCamera.inverse()); // field-to-robot
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return Optional.of(
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new EstimatedRobotPose(
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best,
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cameraResult.getTimestampSeconds(),
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cameraResult.getTargets(),
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|
PoseStrategy.MULTI_TAG_PNP_ON_RIO));
|
|
}
|
|
|
|
/**
|
|
* Return the estimated position of the robot with the lowest position ambiguity from a pipeline
|
|
* result.
|
|
*
|
|
* @param cameraResult A pipeline result from the camera.
|
|
* @return An {@link EstimatedRobotPose} with an estimated pose, timestamp, and targets used to
|
|
* create the estimate, or an empty optional if there's no targets.
|
|
*/
|
|
public Optional<EstimatedRobotPose> estimateLowestAmbiguityPose(
|
|
PhotonPipelineResult cameraResult) {
|
|
if (!shouldEstimate(cameraResult)) {
|
|
return Optional.empty();
|
|
}
|
|
PhotonTrackedTarget lowestAmbiguityTarget = null;
|
|
|
|
double lowestAmbiguityScore = 10;
|
|
|
|
for (PhotonTrackedTarget target : cameraResult.targets) {
|
|
double targetPoseAmbiguity = target.getPoseAmbiguity();
|
|
// Make sure the target is a Fiducial target.
|
|
if (targetPoseAmbiguity != -1 && targetPoseAmbiguity < lowestAmbiguityScore) {
|
|
lowestAmbiguityScore = targetPoseAmbiguity;
|
|
lowestAmbiguityTarget = target;
|
|
}
|
|
}
|
|
|
|
// Although there are confirmed to be targets, none of them may be fiducial
|
|
// targets.
|
|
if (lowestAmbiguityTarget == null) return Optional.empty();
|
|
|
|
int targetFiducialId = lowestAmbiguityTarget.getFiducialId();
|
|
|
|
Optional<Pose3d> targetPosition = fieldTags.getTagPose(targetFiducialId);
|
|
|
|
if (targetPosition.isEmpty()) {
|
|
reportFiducialPoseError(targetFiducialId);
|
|
return Optional.empty();
|
|
}
|
|
|
|
return Optional.of(
|
|
new EstimatedRobotPose(
|
|
targetPosition
|
|
.get()
|
|
.transformBy(lowestAmbiguityTarget.getBestCameraToTarget().inverse())
|
|
.transformBy(robotToCamera.inverse()),
|
|
cameraResult.getTimestampSeconds(),
|
|
cameraResult.getTargets(),
|
|
PoseStrategy.LOWEST_AMBIGUITY));
|
|
}
|
|
|
|
/**
|
|
* Return the estimated position of the robot using the target with the lowest delta height
|
|
* difference between the estimated and actual height of the camera.
|
|
*
|
|
* @param cameraResult A pipeline result from the camera.
|
|
* @return An {@link EstimatedRobotPose} with an estimated pose, timestamp, and targets used to
|
|
* create the estimate, or an empty optional if there's no targets.
|
|
*/
|
|
public Optional<EstimatedRobotPose> estimateClosestToCameraHeightPose(
|
|
PhotonPipelineResult cameraResult) {
|
|
if (!shouldEstimate(cameraResult)) {
|
|
return Optional.empty();
|
|
}
|
|
double smallestHeightDifference = 10e9;
|
|
EstimatedRobotPose closestHeightTarget = null;
|
|
|
|
for (PhotonTrackedTarget target : cameraResult.targets) {
|
|
int targetFiducialId = target.getFiducialId();
|
|
|
|
// Don't report errors for non-fiducial targets. This could also be resolved by
|
|
// adding -1 to
|
|
// the initial HashSet.
|
|
if (targetFiducialId == -1) continue;
|
|
|
|
Optional<Pose3d> targetPosition = fieldTags.getTagPose(target.getFiducialId());
|
|
|
|
if (targetPosition.isEmpty()) {
|
|
reportFiducialPoseError(target.getFiducialId());
|
|
continue;
|
|
}
|
|
|
|
double alternateTransformDelta =
|
|
Math.abs(
|
|
robotToCamera.getZ()
|
|
- targetPosition
|
|
.get()
|
|
.transformBy(target.getAlternateCameraToTarget().inverse())
|
|
.getZ());
|
|
double bestTransformDelta =
|
|
Math.abs(
|
|
robotToCamera.getZ()
|
|
- targetPosition
|
|
.get()
|
|
.transformBy(target.getBestCameraToTarget().inverse())
|
|
.getZ());
|
|
|
|
if (alternateTransformDelta < smallestHeightDifference) {
|
|
smallestHeightDifference = alternateTransformDelta;
|
|
closestHeightTarget =
|
|
new EstimatedRobotPose(
|
|
targetPosition
|
|
.get()
|
|
.transformBy(target.getAlternateCameraToTarget().inverse())
|
|
.transformBy(robotToCamera.inverse()),
|
|
cameraResult.getTimestampSeconds(),
|
|
cameraResult.getTargets(),
|
|
PoseStrategy.CLOSEST_TO_CAMERA_HEIGHT);
|
|
}
|
|
|
|
if (bestTransformDelta < smallestHeightDifference) {
|
|
smallestHeightDifference = bestTransformDelta;
|
|
closestHeightTarget =
|
|
new EstimatedRobotPose(
|
|
targetPosition
|
|
.get()
|
|
.transformBy(target.getBestCameraToTarget().inverse())
|
|
.transformBy(robotToCamera.inverse()),
|
|
cameraResult.getTimestampSeconds(),
|
|
cameraResult.getTargets(),
|
|
PoseStrategy.CLOSEST_TO_CAMERA_HEIGHT);
|
|
}
|
|
}
|
|
|
|
// Need to null check here in case none of the provided targets are fiducial.
|
|
return Optional.ofNullable(closestHeightTarget);
|
|
}
|
|
|
|
/**
|
|
* Return the estimated position of the robot using the target with the lowest delta in the vector
|
|
* magnitude between it and the reference pose.
|
|
*
|
|
* @param cameraResult A pipeline result from the camera.
|
|
* @param referencePose reference pose to check vector magnitude difference against.
|
|
* @return An {@link EstimatedRobotPose} with an estimated pose, timestamp, and targets used to
|
|
* create the estimate, or an empty optional if there's no targets.
|
|
*/
|
|
public Optional<EstimatedRobotPose> estimateClosestToReferencePose(
|
|
PhotonPipelineResult cameraResult, Pose3d referencePose) {
|
|
if (!shouldEstimate(cameraResult)) {
|
|
return Optional.empty();
|
|
}
|
|
if (referencePose == null) {
|
|
DriverStation.reportError(
|
|
"[PhotonPoseEstimator] Tried to use reference pose strategy without setting the reference!",
|
|
false);
|
|
return Optional.empty();
|
|
}
|
|
|
|
double smallestPoseDelta = 10e9;
|
|
EstimatedRobotPose lowestDeltaPose = null;
|
|
|
|
for (PhotonTrackedTarget target : cameraResult.targets) {
|
|
int targetFiducialId = target.getFiducialId();
|
|
|
|
// Don't report errors for non-fiducial targets. This could also be resolved by
|
|
// adding -1 to
|
|
// the initial HashSet.
|
|
if (targetFiducialId == -1) continue;
|
|
|
|
Optional<Pose3d> targetPosition = fieldTags.getTagPose(target.getFiducialId());
|
|
|
|
if (targetPosition.isEmpty()) {
|
|
reportFiducialPoseError(targetFiducialId);
|
|
continue;
|
|
}
|
|
|
|
Pose3d altTransformPosition =
|
|
targetPosition
|
|
.get()
|
|
.transformBy(target.getAlternateCameraToTarget().inverse())
|
|
.transformBy(robotToCamera.inverse());
|
|
Pose3d bestTransformPosition =
|
|
targetPosition
|
|
.get()
|
|
.transformBy(target.getBestCameraToTarget().inverse())
|
|
.transformBy(robotToCamera.inverse());
|
|
|
|
double altDifference = Math.abs(calculateDifference(referencePose, altTransformPosition));
|
|
double bestDifference = Math.abs(calculateDifference(referencePose, bestTransformPosition));
|
|
|
|
if (altDifference < smallestPoseDelta) {
|
|
smallestPoseDelta = altDifference;
|
|
lowestDeltaPose =
|
|
new EstimatedRobotPose(
|
|
altTransformPosition,
|
|
cameraResult.getTimestampSeconds(),
|
|
cameraResult.getTargets(),
|
|
PoseStrategy.CLOSEST_TO_REFERENCE_POSE);
|
|
}
|
|
if (bestDifference < smallestPoseDelta) {
|
|
smallestPoseDelta = bestDifference;
|
|
lowestDeltaPose =
|
|
new EstimatedRobotPose(
|
|
bestTransformPosition,
|
|
cameraResult.getTimestampSeconds(),
|
|
cameraResult.getTargets(),
|
|
PoseStrategy.CLOSEST_TO_REFERENCE_POSE);
|
|
}
|
|
}
|
|
return Optional.ofNullable(lowestDeltaPose);
|
|
}
|
|
|
|
/**
|
|
* Return the average of the best target poses using ambiguity as weight.
|
|
*
|
|
* @param cameraResult A pipeline result from the camera.
|
|
* @return An {@link EstimatedRobotPose} with an estimated pose, timestamp, and targets used to
|
|
* create the estimate, or an empty optional if there's no targets.
|
|
*/
|
|
public Optional<EstimatedRobotPose> estimateAverageBestTargetsPose(
|
|
PhotonPipelineResult cameraResult) {
|
|
if (!shouldEstimate(cameraResult)) {
|
|
return Optional.empty();
|
|
}
|
|
List<Pair<PhotonTrackedTarget, Pose3d>> estimatedRobotPoses = new ArrayList<>();
|
|
double totalAmbiguity = 0;
|
|
|
|
for (PhotonTrackedTarget target : cameraResult.targets) {
|
|
int targetFiducialId = target.getFiducialId();
|
|
|
|
// Don't report errors for non-fiducial targets. This could also be resolved by
|
|
// adding -1 to
|
|
// the initial HashSet.
|
|
if (targetFiducialId == -1) continue;
|
|
|
|
Optional<Pose3d> targetPosition = fieldTags.getTagPose(target.getFiducialId());
|
|
|
|
if (targetPosition.isEmpty()) {
|
|
reportFiducialPoseError(targetFiducialId);
|
|
continue;
|
|
}
|
|
|
|
double targetPoseAmbiguity = target.getPoseAmbiguity();
|
|
|
|
// Pose ambiguity is 0, use that pose
|
|
if (targetPoseAmbiguity == 0) {
|
|
return Optional.of(
|
|
new EstimatedRobotPose(
|
|
targetPosition
|
|
.get()
|
|
.transformBy(target.getBestCameraToTarget().inverse())
|
|
.transformBy(robotToCamera.inverse()),
|
|
cameraResult.getTimestampSeconds(),
|
|
cameraResult.getTargets(),
|
|
PoseStrategy.AVERAGE_BEST_TARGETS));
|
|
}
|
|
|
|
totalAmbiguity += 1.0 / target.getPoseAmbiguity();
|
|
|
|
estimatedRobotPoses.add(
|
|
new Pair<>(
|
|
target,
|
|
targetPosition
|
|
.get()
|
|
.transformBy(target.getBestCameraToTarget().inverse())
|
|
.transformBy(robotToCamera.inverse())));
|
|
}
|
|
|
|
// Take the average
|
|
|
|
Translation3d transform = new Translation3d();
|
|
Rotation3d rotation = new Rotation3d();
|
|
|
|
if (estimatedRobotPoses.isEmpty()) return Optional.empty();
|
|
|
|
for (Pair<PhotonTrackedTarget, Pose3d> pair : estimatedRobotPoses) {
|
|
// Total ambiguity is non-zero confirmed because if it was zero, that pose was
|
|
// returned.
|
|
double weight = (1.0 / pair.getFirst().getPoseAmbiguity()) / totalAmbiguity;
|
|
Pose3d estimatedPose = pair.getSecond();
|
|
transform = transform.plus(estimatedPose.getTranslation().times(weight));
|
|
rotation = rotation.rotateBy(estimatedPose.getRotation().times(weight));
|
|
}
|
|
|
|
return Optional.of(
|
|
new EstimatedRobotPose(
|
|
new Pose3d(transform, rotation),
|
|
cameraResult.getTimestampSeconds(),
|
|
cameraResult.getTargets(),
|
|
PoseStrategy.AVERAGE_BEST_TARGETS));
|
|
}
|
|
|
|
/**
|
|
* Difference is defined as the vector magnitude between the two poses
|
|
*
|
|
* @return The absolute "difference" (>=0) between two Pose3ds.
|
|
*/
|
|
private double calculateDifference(Pose3d x, Pose3d y) {
|
|
return x.getTranslation().getDistance(y.getTranslation());
|
|
}
|
|
|
|
private void reportFiducialPoseError(int fiducialId) {
|
|
if (!reportedErrors.contains(fiducialId)) {
|
|
DriverStation.reportError(
|
|
"[PhotonPoseEstimator] Tried to get pose of unknown AprilTag: " + fiducialId, false);
|
|
reportedErrors.add(fiducialId);
|
|
}
|
|
}
|
|
}
|