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995 lines
47 KiB
995 lines
47 KiB
#!/usr/bin/env python
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from __future__ import print_function
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import calibration as ca
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from sensor_msgs.msg import JointState
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from geometry_msgs.msg import Twist
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from nav_msgs.msg import Odometry
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import rospy
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import actionlib
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from control_msgs.msg import FollowJointTrajectoryAction
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from control_msgs.msg import FollowJointTrajectoryGoal
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from trajectory_msgs.msg import JointTrajectoryPoint
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from visualization_msgs.msg import Marker
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from visualization_msgs.msg import MarkerArray
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from tf.transformations import quaternion_from_euler, euler_from_quaternion, quaternion_from_matrix
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from sensor_msgs.msg import Imu
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from geometry_msgs.msg import Point
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from geometry_msgs.msg import Pose
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import math
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import time
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import threading
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import sys
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import numpy as np
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import ros_numpy
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from copy import deepcopy
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import yaml
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import time
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import glob
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import argparse as ap
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from urdf_parser_py.urdf import URDF as urdf_parser
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import urdf_parser_py as up
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from scipy.spatial.transform import Rotation
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import cma
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from visualization_msgs.msg import Marker
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from visualization_msgs.msg import MarkerArray
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class HeadCalibrator:
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def __init__(self, uncalibrated_urdf_filename, calibration_directory, sample_selector_func, calibration_options, visualize, tilt_angle_backlash_transition_rad):
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self.visualize = visualize
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self.infinite_duration_visualization = False
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self.observations_used_for_fit = {}
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self.telescoping_joints = ['joint_arm_l0', 'joint_arm_l1', 'joint_arm_l2', 'joint_arm_l3']
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self.tilt_angle_backlash_transition_rad = tilt_angle_backlash_transition_rad
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self.calibrate_lift = calibration_options.get('calibrate_lift', False)
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self.calibrate_arm = calibration_options.get('calibrate_arm', False)
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self.calibrate_controller_offsets = calibration_options.get('calibrate_controller_offsets', False)
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self.calibrate_pan_backlash = calibration_options.get('calibrate_pan_backlash', False)
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self.calibrate_tilt_backlash = calibration_options.get('calibrate_tilt_backlash', False)
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self.calibrate_arm_backlash = calibration_options.get('calibrate_arm_backlash', False)
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self.default_backlash_state = {'joint_head_pan_looked_left':False, 'joint_head_tilt_looking_up':False, 'wrist_extension_retracted':False}
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self.use_this_sample = sample_selector_func
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self.calibration_directory = calibration_directory
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self.head_calibration_data_filename = None
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if self.visualize:
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self.visualization_markers_pub = rospy.Publisher('/calibration/marker_array', MarkerArray, queue_size=1)
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else:
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self.visualization_markers_pub = None
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self.pan_angle_offset = 0.0
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self.tilt_angle_offset = 0.0
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self.pan_looked_left_offset = 0.0
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self.tilt_looking_up_offset = 0.0
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self.arm_retracted_offset = 0.0
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self.initial_error_terms = {'wrist_top_pos': 0.0,
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'wrist_top_orient': 0.0,
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'wrist_inside_pos': 0.0,
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'wrist_inside_orient': 0.0,
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'shoulder_pos': 0.0,
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'shoulder_orient': 0.0,
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'base_left_pos': 0.0,
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'base_left_orient': 0.0,
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'base_right_pos': 0.0,
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'base_right_orient': 0.0,
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'parameter_error_total': 0.0}
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self.error_weights = {'wrist_top_pos': 1.0,
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'wrist_top_orient': 0.1,
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'wrist_inside_pos': 1.0,
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'wrist_inside_orient': 0.1,
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'shoulder_pos': 0.5,
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'shoulder_orient': 0.05,
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'base_left_pos': 0.5,
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'base_left_orient': 0.05,
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'base_right_pos': 0.5,
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'base_right_orient': 0.05,
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'joint_head_tilt_looking_up_error_multiplier': 2.0,
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'parameter_error_total': 1.0}
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# Load and store the original uncalibrated URDF.
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self.original_urdf = urdf_parser.from_xml_file(uncalibrated_urdf_filename)
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# Initialize the new URDF that will altered during optimization.
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self.new_urdf = deepcopy(self.original_urdf)
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self.gravity_acceleration_vec = np.array([0.0, 0.0, 1.0])
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##########
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# Set the kinematic chains and joints to be used during
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# optimization. These *_joint and *_chain variables are used
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# to access and modify the optimized URDF (self.new_urdf)
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# during optimization.
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# Also, set the original joint positions and rotations. The
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# optimization applies rigid body transformations to these
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# original poses.
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# Chain from the base of the robot to the color camera used to
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# observe the ARUCO markers.
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self.camera_chain = ca.Chain(self.new_urdf, 'base_link', 'camera_color_optical_frame')
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# This is the part that connects the pan joint to the top of
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# the mast. There is some variation in how it is mounted to
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# the top of the mast. For example, it can be rotated
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# slightly.
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self.camera_tilt_joint = self.camera_chain.get_joint_by_name('joint_head_tilt')
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self.original_tilt_assembly_xyz = np.array(self.camera_tilt_joint.origin.xyz)
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self.original_tilt_assembly_rpy = np.array(self.camera_tilt_joint.origin.rpy)
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self.original_tilt_assembly_r = Rotation.from_euler('xyz', self.original_tilt_assembly_rpy)
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self.camera_pan_joint = self.camera_chain.get_joint_by_name('joint_head_pan')
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self.original_pan_assembly_xyz = np.array(self.camera_pan_joint.origin.xyz)
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self.original_pan_assembly_rpy = np.array(self.camera_pan_joint.origin.rpy)
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self.original_pan_assembly_r = Rotation.from_euler('xyz', self.original_pan_assembly_rpy)
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self.camera_mount_joint = self.camera_chain.get_joint_by_name('camera_joint')
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self.original_camera_mount_xyz = np.array(self.camera_mount_joint.origin.xyz)
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self.original_camera_mount_rpy = np.array(self.camera_mount_joint.origin.rpy)
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self.original_camera_mount_r = Rotation.from_euler('xyz', self.original_camera_mount_rpy)
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# Chain from the base of the robot to robot's wrist that has ARUCO markers on it.
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self.wrist_chain = ca.Chain(self.new_urdf, 'base_link', 'link_arm_l0')
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if self.calibrate_lift:
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# Calibrating the following joint ('joint_mast') results
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# in better visualizations, since it actually moves the
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# mast and keeps the carriage traveling along the
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# mast. Without constraints, this should permit the same
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# solutions as calibrating 'joint_lift'. With constraints,
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# the possible solutions can differ due to the mast also
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# influencing the position and orientation of the head
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# assembly.
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self.lift_joint = self.wrist_chain.get_joint_by_name('joint_mast')
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self.original_lift_xyz = np.array(self.lift_joint.origin.xyz)
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self.original_lift_rpy = np.array(self.lift_joint.origin.rpy)
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self.original_lift_r = Rotation.from_euler('xyz', self.original_lift_rpy)
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if self.calibrate_arm:
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# This calibrates the position and orientation of the arm
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# with respect to the shoulder carriage that moves up and
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# down the lift. This compensates for variations in the
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# way the arm has been mounted.
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self.arm_joint = self.wrist_chain.get_joint_by_name('joint_arm_l4')
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self.original_arm_xyz = np.array(self.arm_joint.origin.xyz)
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self.original_arm_rpy = np.array(self.arm_joint.origin.rpy)
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self.original_arm_r = Rotation.from_euler('xyz', self.original_arm_rpy)
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##########
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# calculate 10 deg of error
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deg_error = 10.0
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rad_error = 10.0 * (math.pi/180.0)
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calib_error = math.cos(rad_error)
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calib_error = (1.0 - calib_error) / 2.0
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# convert error to meters of error per degree
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self.meters_per_deg = 0.02 / calib_error # 2cm of error per 10 deg
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self.error_measures = []
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# Create error measures to be used during the optimization.
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aruco_urdf = self.new_urdf
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rgba = [0.0, 1.0, 0.0, 0.2]
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self.error_measures.append(ca.ArucoError('wrist_top', 'wrist', 'link_aruco_top_wrist', aruco_urdf, self.meters_per_deg, rgba))
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rgba = [0.0, 0.0, 1.0, 0.2]
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self.error_measures.append(ca.ArucoError('wrist_inside', 'wrist', 'link_aruco_inner_wrist', aruco_urdf, self.meters_per_deg, rgba))
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rgba = [1.0, 1.0, 0.0, 0.2]
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self.error_measures.append(ca.ArucoError('shoulder', 'shoulder', 'link_aruco_shoulder', aruco_urdf, self.meters_per_deg, rgba))
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rgba = [1.0, 0.0, 0.0, 0.2]
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self.error_measures.append(ca.ArucoError('base_left', 'base', 'link_aruco_left_base', aruco_urdf, self.meters_per_deg, rgba))
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rgba = [1.0, 0.0, 1.0, 0.2]
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self.error_measures.append(ca.ArucoError('base_right', 'base', 'link_aruco_right_base', aruco_urdf, self.meters_per_deg, rgba))
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def load_data(self, parameters, use_check_calibration_data=False):
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# Load data to use for calibration.
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if use_check_calibration_data:
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filenames = glob.glob(self.calibration_directory + 'check_head_calibration_data' + '_*[0-9].yaml')
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else:
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filenames = glob.glob(self.calibration_directory + 'head_calibration_data' + '_*[0-9].yaml')
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filenames.sort()
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most_recent_filename = filenames[-1]
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self.head_calibration_data_filename = most_recent_filename
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print('Loading most recent head calibration data from a YAML file named ' + self.head_calibration_data_filename)
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fid = open(self.head_calibration_data_filename, 'r')
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self.data = yaml.load(fid)
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fid.close()
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# Convert data in yaml file from readable text to full joint
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# state representation and ROS Poses
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marker_prefixes = ['wrist_top', 'wrist_inside', 'shoulder', 'base_left', 'base_right']
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for i, s in enumerate(self.data):
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# Convert from wrist_extension to joint links for compatibility with the URDF
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joints = s['joints']
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# convert wrist_extension to telescoping joints for use with the URDF
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wrist_extension = joints.pop('wrist_extension')
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telescoping_value = wrist_extension/float(len(self.telescoping_joints))
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for j in self.telescoping_joints:
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joints[j] = telescoping_value
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# Convert from 4x4 homogeneous transformation matrices
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# represented as lists to ROS Poses
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camera_measurements = s['camera_measurements']
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for p in marker_prefixes:
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pose_key = p + '_marker_pose'
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pose_list = camera_measurements.get(pose_key)
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if pose_list is not None:
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pose = ros_numpy.msgify(Pose, np.array(pose_list))
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camera_measurements[pose_key] = pose
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self.calculate_normalization()
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if self.visualize:
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self.update_urdf(parameters)
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self.target_markers = self.generate_target_visualizations()
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marker_array = MarkerArray()
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marker_array.markers.extend(self.target_markers)
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self.visualization_markers_pub.publish(marker_array)
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def get_calibration_options(self):
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# Return the current calibration options
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calibration_options = {'calibrate_lift': self.calibrate_lift,
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'calibrate_arm': self.calibrate_arm,
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'calibrate_controller_offsets': self.calibrate_controller_offsets,
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'calibrate_pan_backlash': self.calibrate_pan_backlash,
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'calibrate_tilt_backlash': self.calibrate_tilt_backlash,
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'calibrate_arm_backlash': self.calibrate_arm_backlash}
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return calibration_options
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def get_names_of_parameters_to_fit(self):
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# Return the names of the parameters being fit by the optimization.
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parameter_names = []
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if self.calibrate_controller_offsets:
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parameter_names.extend(['pan_angle_offset', 'tilt_angle_offset'])
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if self.calibrate_pan_backlash:
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parameter_names.extend(['pan_looked_left_offset'])
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if self.calibrate_tilt_backlash:
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parameter_names.extend(['tilt_looking_up_offset'])
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if self.calibrate_arm_backlash:
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parameter_names.extend(['arm_retracted_offset'])
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parameter_names.extend(['head_assembly_offset_xyz_x', 'head_assembly_offset_xyz_y', 'head_assembly_offset_xyz_z',
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'head_assembly_offset_rotvec_x', 'head_assembly_offset_rotvec_y', 'head_assembly_offset_rotvec_z',
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'pan_assembly_offset_xyz_x', 'pan_assembly_offset_xyz_y', 'pan_assembly_offset_xyz_z',
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'pan_assembly_offset_rotvec_x', 'pan_assembly_offset_rotvec_y', 'pan_assembly_offset_rotvec_z',
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'tilt_assembly_offset_xyz_x', 'tilt_assembly_offset_xyz_y', 'tilt_assembly_offset_xyz_z',
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'tilt_assembly_offset_rotvec_x', 'tilt_assembly_offset_rotvec_y', 'tilt_assembly_offset_rotvec_z',
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'camera_mount_offset_xyz_x', 'camera_mount_offset_xyz_y', 'camera_mount_offset_xyz_z',
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'camera_mount_offset_rotvec_x', 'camera_mount_offset_rotvec_y', 'camera_mount_offset_rotvec_z'])
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if self.calibrate_lift:
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parameter_names.extend(['lift_offset_xyz_x', 'lift_offset_xyz_y', 'lift_offset_xyz_z',
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'lift_offset_rotvec_x', 'lift_offset_rotvec_y', 'lift_offset_rotvec_z'])
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if self.calibrate_arm:
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parameter_names.extend(['arm_offset_xyz_x', 'arm_offset_xyz_y', 'arm_offset_xyz_z',
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'arm_offset_rotvec_x', 'arm_offset_rotvec_y', 'arm_offset_rotvec_z'])
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return parameter_names
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def get_controller_parameters(self):
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return {'tilt_angle_offset': self.tilt_angle_offset,
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'pan_angle_offset': self.pan_angle_offset,
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'pan_looked_left_offset': self.pan_looked_left_offset,
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'tilt_looking_up_offset': self.tilt_looking_up_offset,
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'arm_retracted_offset': self.arm_retracted_offset,
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'tilt_angle_backlash_transition': self.tilt_angle_backlash_transition_rad}
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def parameter_error_term(self, parameters):
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# Calculate error that is solely a function of the parameter
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# values. This is used to keep parameters within desired
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# ranges by assigning high errors when parameters are outside
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# these ranges using a soft constraint function.
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###########
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# Unpack and preprocess the parameters
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i = 0
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if self.calibrate_controller_offsets:
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pan_angle_offset_abs = abs(parameters[i])
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i += 1
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tilt_angle_offset_abs = abs(parameters[i])
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i += 1
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if self.calibrate_pan_backlash:
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pan_looked_left_offset_abs = abs(parameters[i])
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i += 1
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if self.calibrate_tilt_backlash:
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tilt_looking_up_offset_abs = abs(parameters[i])
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i += 1
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if self.calibrate_arm_backlash:
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arm_retracted_offset_abs = abs(parameters[i])
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i += 1
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head_assembly_offset_xyz_length = np.linalg.norm(parameters[i:i+3])
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i += 3
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head_assembly_offset_rotvec_mag = np.linalg.norm(parameters[i:i+3])
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i += 3
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pan_assembly_offset_xyz_length = np.linalg.norm(parameters[i:i+3])
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i += 3
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pan_assembly_offset_rotvec_mag = np.linalg.norm(parameters[i:i+3])
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i += 3
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tilt_assembly_offset_xyz_length = np.linalg.norm(parameters[i:i+3])
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i += 3
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tilt_assembly_offset_rotvec_mag = np.linalg.norm(parameters[i:i+3])
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i += 3
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camera_mount_offset_xyz_length = np.linalg.norm(parameters[i:i+3])
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i += 3
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camera_mount_offset_rotvec_mag = np.linalg.norm(parameters[i:i+3])
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i += 3
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if self.calibrate_lift:
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lift_offset_xyz_length = np.linalg.norm(parameters[i:i+3])
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i += 3
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lift_offset_rotvec_mag = np.linalg.norm(parameters[i:i+3])
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i += 3
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if self.calibrate_arm:
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arm_offset_xyz_length = np.linalg.norm(parameters[i:i+3])
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i += 3
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arm_offset_rotvec_mag = np.linalg.norm(parameters[i:i+3])
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i += 3
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###########
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# Compute the individual parameter errors and sum them up.
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parameter_error = 0.0
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if self.calibrate_controller_offsets:
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# no penalty region: +/- 10 deg for the pan and tilt angle
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# offsets (10 deg = math.pi/18.0 rad)
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parameter_error += ca.soft_constraint_error(pan_angle_offset_abs, math.pi/18.0, 10.0)
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parameter_error += ca.soft_constraint_error(tilt_angle_offset_abs, math.pi/18.0, 10.0)
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if self.calibrate_pan_backlash:
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# no penalty region: +/- 5 deg for the pan and tilt angle
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# offsets (5 deg = math.pi/36.0 rad)
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parameter_error += ca.soft_constraint_error(pan_looked_left_offset_abs, math.pi/36.0, 10.0)
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i += 1
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if self.calibrate_tilt_backlash:
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# no penalty region: +/- 5 deg for the pan and tilt angle
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# offsets (5 deg = math.pi/36.0 rad)
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parameter_error += ca.soft_constraint_error(tilt_looking_up_offset_abs, math.pi/36.0, 10.0)
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i += 1
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if self.calibrate_arm_backlash:
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# no penalty region: +/- 1 cm
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parameter_error += ca.soft_constraint_error(arm_retracted_offset_abs, 0.01, 10.0)
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i += 1
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# no penalty region: 0.5cm radius sphere within which the head
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# assembly can be placed relative to its uncalibrated position
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# with respect to the mast. 5 rotation of the head (5 deg =
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# 180/36 deg = math.pi/36.0 rad)
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parameter_error += ca.soft_constraint_error(head_assembly_offset_xyz_length, 0.005, 10.0)
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parameter_error += ca.soft_constraint_error(head_assembly_offset_rotvec_mag, math.pi/36.0, 10.0)
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# no penalty region: 1cm radius sphere within which the pan
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# assembly can be placed relative to its uncalibrated position
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# with respect to the mast. 5 degree max rotation of the pan
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# assembly (5 deg = 180/36 deg = math.pi/36.0 rad)
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parameter_error += ca.soft_constraint_error(pan_assembly_offset_xyz_length, 0.01, 10.0)
|
|
parameter_error += ca.soft_constraint_error(pan_assembly_offset_rotvec_mag, math.pi/36.0, 10.0)
|
|
|
|
# no penalty region: 1cm radius sphere within which the tilt
|
|
# assembly can be placed relative to its uncalibrated position
|
|
# with respect to the pan assembly. 5 degree max rotation of
|
|
# the tilt assembly. (5 deg = 180/36 deg = math.pi/36.0 rad)
|
|
parameter_error += ca.soft_constraint_error(tilt_assembly_offset_xyz_length, 0.01, 10.0)
|
|
parameter_error += ca.soft_constraint_error(tilt_assembly_offset_rotvec_mag, math.pi/36.0, 10.0)
|
|
|
|
# no penalty region: 0.5cm radius sphere within which the
|
|
# camera can be placed relative to its uncalibrated position
|
|
# with respect to the pan assembly. 5 max rotation of the
|
|
# camera assembly. (5 deg = 180/36 deg = math.pi/36.0 rad)
|
|
parameter_error += ca.soft_constraint_error(camera_mount_offset_xyz_length, 0.005, 10.0)
|
|
parameter_error += ca.soft_constraint_error(camera_mount_offset_rotvec_mag, math.pi/36.0, 10.0)
|
|
|
|
if self.calibrate_lift:
|
|
# no penalty region: 1cm radius sphere within which the
|
|
# mast can be placed relative to its uncalibrated position
|
|
# with respect to the mobile base. 1.5 degree max rotation
|
|
# of the mast relative to the mobile base. (2.5 deg =
|
|
# 180/72 deg = math.pi/72.0 rad)
|
|
parameter_error += ca.soft_constraint_error(lift_offset_xyz_length, 0.01, 10.0)
|
|
parameter_error += ca.soft_constraint_error(lift_offset_rotvec_mag, math.pi/72.0, 10.0)
|
|
|
|
if self.calibrate_arm:
|
|
# no penalty region: 1cm radius sphere within which the
|
|
# arm mount can be placed relative to its uncalibrated
|
|
# position with respect to the shoulder. 5 degree max
|
|
# rotation of the arm relative to the shoulder carriage (5
|
|
# deg = 180/36 deg = math.pi/36.0 rad).
|
|
parameter_error += ca.soft_constraint_error(arm_offset_xyz_length, 0.01, 10.0)
|
|
parameter_error += ca.soft_constraint_error(arm_offset_rotvec_mag, math.pi/36.0, 10.0)
|
|
|
|
output_error = {'total': parameter_error}
|
|
return output_error
|
|
|
|
|
|
def update_urdf(self, parameters):
|
|
# This updates self.new_urdf via the camera's kinematic chain.
|
|
|
|
i = 0
|
|
|
|
if self.calibrate_controller_offsets:
|
|
self.pan_angle_offset = parameters[i]
|
|
i += 1
|
|
self.tilt_angle_offset = parameters[i]
|
|
i += 1
|
|
else:
|
|
self.pan_angle_offset = 0.0
|
|
self.tilt_angle_offset = 0.0
|
|
|
|
if self.calibrate_pan_backlash:
|
|
self.pan_looked_left_offset = parameters[i]
|
|
i += 1
|
|
|
|
if self.calibrate_tilt_backlash:
|
|
self.tilt_looking_up_offset = parameters[i]
|
|
i += 1
|
|
|
|
if self.calibrate_arm_backlash:
|
|
self.arm_retracted_offset = parameters[i]
|
|
i += 1
|
|
|
|
head_assembly_offset_xyz = parameters[i:i+3]
|
|
i += 3
|
|
head_assembly_offset_rotvec = parameters[i:i+3]
|
|
i += 3
|
|
|
|
pan_assembly_offset_xyz = parameters[i:i+3]
|
|
i += 3
|
|
pan_assembly_offset_rotvec = parameters[i:i+3]
|
|
i += 3
|
|
|
|
tilt_assembly_offset_xyz = parameters[i:i+3]
|
|
i += 3
|
|
tilt_assembly_offset_rotvec = parameters[i:i+3]
|
|
i += 3
|
|
|
|
camera_mount_offset_xyz = parameters[i:i+3]
|
|
i += 3
|
|
camera_mount_offset_rotvec = parameters[i:i+3]
|
|
i += 3
|
|
|
|
if self.calibrate_lift:
|
|
lift_offset_xyz = parameters[i:i+3]
|
|
i += 3
|
|
lift_offset_rotvec = parameters[i:i+3]
|
|
i += 3
|
|
|
|
if self.calibrate_arm:
|
|
arm_offset_xyz = parameters[i:i+3]
|
|
i += 3
|
|
arm_offset_rotvec = parameters[i:i+3]
|
|
i += 3
|
|
|
|
self.camera_pan_joint.origin.xyz = self.original_pan_assembly_xyz + pan_assembly_offset_xyz
|
|
pan_assembly_r = Rotation.from_rotvec(pan_assembly_offset_rotvec) * self.original_pan_assembly_r
|
|
self.camera_pan_joint.origin.rpy = pan_assembly_r.as_euler('xyz')
|
|
|
|
self.camera_tilt_joint.origin.xyz = self.original_tilt_assembly_xyz + tilt_assembly_offset_xyz
|
|
tilt_assembly_r = Rotation.from_rotvec(tilt_assembly_offset_rotvec) * self.original_tilt_assembly_r
|
|
self.camera_tilt_joint.origin.rpy = tilt_assembly_r.as_euler('xyz')
|
|
|
|
self.camera_mount_joint.origin.xyz = self.original_camera_mount_xyz + camera_mount_offset_xyz
|
|
camera_mount_r = Rotation.from_rotvec(camera_mount_offset_rotvec) * self.original_camera_mount_r
|
|
self.camera_mount_joint.origin.rpy = camera_mount_r.as_euler('xyz')
|
|
|
|
if self.calibrate_lift:
|
|
self.lift_joint.origin.xyz = self.original_lift_xyz + lift_offset_xyz
|
|
lift_r = Rotation.from_rotvec(lift_offset_rotvec) * self.original_lift_r
|
|
self.lift_joint.origin.rpy = lift_r.as_euler('xyz')
|
|
|
|
if self.calibrate_arm:
|
|
self.arm_joint.origin.xyz = self.original_arm_xyz + arm_offset_xyz
|
|
arm_r = Rotation.from_rotvec(arm_offset_rotvec) * self.original_arm_r
|
|
self.arm_joint.origin.rpy = arm_r.as_euler('xyz')
|
|
|
|
|
|
def calculate_normalization(self):
|
|
# Finds the number of observations that will be used for each error measure
|
|
for e in self.error_measures:
|
|
e.reset_observation_count()
|
|
for i, s in enumerate(self.data):
|
|
if self.use_this_sample(i, s):
|
|
for e in self.error_measures:
|
|
e.increment_observation_count(s)
|
|
print('Number of observations of each error measurement type to be used from the data:')
|
|
self.observations_used_for_fit = {}
|
|
for e in self.error_measures:
|
|
print(e.name, '=', e.number_of_observations)
|
|
self.observations_used_for_fit[e.name] = e.number_of_observations
|
|
|
|
def generate_target_visualizations(self):
|
|
target_markers = []
|
|
marker_id = 333333
|
|
marker_time = rospy.Time.now()
|
|
# infinite lifetime (I think.)
|
|
#lifetime = rospy.Duration()
|
|
# 10 second lifetime, which assumes that these will be periodically resent.
|
|
lifetime = rospy.Duration(10.0)
|
|
rgba = [1.0, 1.0, 1.0, 1.0]
|
|
|
|
for i, s in enumerate(self.data):
|
|
if self.use_this_sample(i, s):
|
|
|
|
sample = deepcopy(s)
|
|
|
|
# Update the sample to account for controller
|
|
# parameters and backlash. Specifically, this adds pan
|
|
# and tilt offsets and backlash state dependent
|
|
# offsets to pan, tilt, and the arm extension.
|
|
self.update_sample_with_offsets_and_backlash(sample)
|
|
|
|
for e in self.error_measures:
|
|
markers, marker_id = e.get_target_ros_markers(sample, marker_id, marker_time, rgba, self.gravity_acceleration_vec)
|
|
for m in markers:
|
|
unique = True
|
|
for current_m in target_markers:
|
|
same_type = (m.type == current_m.type)
|
|
same_pose = (m.pose == current_m.pose)
|
|
same_points = (m.points == current_m.points)
|
|
if same_type and same_pose and same_points:
|
|
unique = False
|
|
if unique:
|
|
m.lifetime = lifetime
|
|
target_markers.append(m)
|
|
# Note that there will be many target markers for markers on a
|
|
# kinematic chain sensitive to the joint values, such as the
|
|
# ArUco markers on the wrist. In contrast, the ArUco markers
|
|
# on the mobile base will only have a single unique marker.
|
|
return target_markers
|
|
|
|
|
|
def visualize_fit(self, parameters_to_fit):
|
|
self.visualize = True
|
|
self.infinite_duration_visualization = True
|
|
self.calculate_error(parameters_to_fit)
|
|
|
|
|
|
def update_sample_with_offsets_and_backlash(self, sample):
|
|
# This changes the sample passed as an argument using
|
|
# controller parameters and the backlash state. Specifically,
|
|
# it adds pan and tilt offsets and backlash state dependent
|
|
# offsets to pan, tilt, and the arm extension.
|
|
|
|
backlash_state = sample.get('backlash_state', self.default_backlash_state)
|
|
|
|
if backlash_state['joint_head_pan_looked_left']:
|
|
pan_backlash_correction = self.pan_looked_left_offset
|
|
else:
|
|
pan_backlash_correction = 0.0
|
|
|
|
if backlash_state['joint_head_tilt_looking_up']:
|
|
#tilt_backlash_correction = -math.pi/18.0 # 10 deg test
|
|
tilt_backlash_correction = self.tilt_looking_up_offset
|
|
else:
|
|
tilt_backlash_correction = 0.0
|
|
|
|
if backlash_state['wrist_extension_retracted']:
|
|
arm_backlash_correction = self.arm_retracted_offset
|
|
else:
|
|
arm_backlash_correction = 0.0
|
|
|
|
joints = sample['joints']
|
|
for j in self.telescoping_joints:
|
|
joints[j] = joints[j] + (arm_backlash_correction/4.0)
|
|
|
|
joints['joint_head_pan'] = joints['joint_head_pan'] + self.pan_angle_offset + pan_backlash_correction
|
|
joints['joint_head_tilt'] = joints['joint_head_tilt'] + self.tilt_angle_offset + tilt_backlash_correction
|
|
|
|
|
|
def calculate_error(self, parameters_to_fit, output_error_terms=False):
|
|
|
|
error_terms = self.initial_error_terms.copy()
|
|
|
|
error_terms['parameter_error_total'] += self.parameter_error_term(parameters_to_fit)['total']
|
|
|
|
# updates the URDF and fit method variables with the current
|
|
# parameters
|
|
self.update_urdf(parameters_to_fit)
|
|
|
|
marker_array = MarkerArray()
|
|
marker_time = rospy.Time.now()
|
|
marker_id = 0
|
|
|
|
if self.visualize:
|
|
# Send the target markers on each iteration, since they
|
|
# can be changed by some of the fit parameters, such as
|
|
# backlash compensation for arm retraction.
|
|
self.target_markers = self.generate_target_visualizations()
|
|
marker_array.markers.extend(self.target_markers)
|
|
|
|
for i, s in enumerate(self.data):
|
|
|
|
if self.use_this_sample(i, s):
|
|
|
|
# Do not alter the original data.
|
|
sample = deepcopy(s)
|
|
backlash_state = sample.get('backlash_state', self.default_backlash_state)
|
|
|
|
# Update the sample to account for controller
|
|
# parameters and backlash. Specifically, this adds pan
|
|
# and tilt offsets and backlash state dependent
|
|
# offsets to pan, tilt, and the arm extension.
|
|
self.update_sample_with_offsets_and_backlash(sample)
|
|
|
|
# Update each marker, including whether it was
|
|
# detected and the joint values used to compute the
|
|
# URDF's marker prediction in the world frame (not
|
|
# through the camera).
|
|
for e in self.error_measures:
|
|
e.update(sample, marker_time, self.gravity_acceleration_vec)
|
|
|
|
# Find the transform from the camera's coordinate
|
|
# system to the world frame, since all errors are
|
|
# computed in the world frame.
|
|
joints = sample['joints']
|
|
self.camera_transform = self.camera_chain.get_affine_matrix(joints)
|
|
|
|
# If the wrist has a large downward deflection due to
|
|
# a heavy tool, this can potentially be useful. It
|
|
# ignores z-axis error for wrist markers when the arm
|
|
# is extended past a defined length.
|
|
#
|
|
# wrist_extension = reduce(lambda current_sum, joint_name: current_sum + joints[joint_name], self.telescoping_joints, 0.0)
|
|
# max_wrist_extension_for_z_fit = 0.13
|
|
# if wrist_extension > max_wrist_extension_for_z_fit:
|
|
# fit_z = False
|
|
# else:
|
|
# fit_z = True
|
|
fit_z = True
|
|
|
|
# Whether or not to use ArUco marker orientation in
|
|
# the objective function.
|
|
#fit_orientation = False
|
|
fit_orientation = True
|
|
|
|
for e in self.error_measures:
|
|
if e.detected:
|
|
if e.location == 'wrist':
|
|
delta_error, ros_markers, marker_id = e.error(self.camera_transform, fit_z, fit_orientation, marker_id, self.visualize)
|
|
else:
|
|
delta_error, ros_markers, marker_id = e.error(self.camera_transform, True, fit_orientation, marker_id, self.visualize)
|
|
|
|
if backlash_state['joint_head_tilt_looking_up']:
|
|
error_multiplier = self.error_weights['joint_head_tilt_looking_up_error_multiplier']
|
|
else:
|
|
error_multiplier = 1.0
|
|
|
|
for d in delta_error:
|
|
error_term_name = e.name + '_' + d
|
|
error_terms[error_term_name] += (error_multiplier * delta_error[d])
|
|
marker_array.markers.extend(ros_markers)
|
|
|
|
total_error = 0.0
|
|
for k in error_terms:
|
|
total_error += self.error_weights[k] * error_terms[k]
|
|
print('error_terms =', error_terms)
|
|
print('total_error =', total_error)
|
|
|
|
if self.visualize:
|
|
if self.infinite_duration_visualization:
|
|
for m in marker_array.markers:
|
|
m.lifetime = rospy.Duration()
|
|
self.visualization_markers_pub.publish(marker_array)
|
|
|
|
if output_error_terms:
|
|
return total_error, error_terms
|
|
|
|
return total_error
|
|
|
|
|
|
def save_urdf_file(self, time_string=None):
|
|
if time_string is None:
|
|
time_string = ca.create_time_string()
|
|
urdf_string = self.new_urdf.to_xml_string()
|
|
filename = self.calibration_directory + 'head_calibrated_' + time_string + '.urdf'
|
|
print('Saving new URDF file to ', filename)
|
|
fid = open(filename, 'w')
|
|
fid.write(urdf_string)
|
|
fid.close()
|
|
print('Finished saving')
|
|
|
|
|
|
def save_controller_calibration_file(self, time_string=None):
|
|
if time_string is None:
|
|
time_string = ca.create_time_string()
|
|
|
|
controller_parameters = self.get_controller_parameters()
|
|
|
|
no_numpy_controller_parameters = {}
|
|
for key, entry in controller_parameters.viewitems():
|
|
if "tolist" in dir(entry):
|
|
entry = entry.tolist()
|
|
no_numpy_controller_parameters[key] = entry
|
|
|
|
filename = self.calibration_directory + 'controller_calibration_head_' + time_string + '.yaml'
|
|
print('Saving controller calibration head parameters to a YAML file named ', filename)
|
|
fid = open(filename, 'w')
|
|
yaml.dump(no_numpy_controller_parameters, fid)
|
|
fid.close()
|
|
print('Finished saving.')
|
|
|
|
|
|
|
|
class ProcessHeadCalibrationDataNode:
|
|
|
|
def __init__(self, opt_results_file_to_load=None, load_most_recent_opt_results=False, visualize_only=False, visualize=True):
|
|
self.opt_results_file_to_load = opt_results_file_to_load
|
|
self.load_most_recent_opt_results = load_most_recent_opt_results
|
|
self.visualize_only = visualize_only
|
|
self.visualize = visualize
|
|
self.loaded_data = None
|
|
|
|
# different error tolerances for different speeds and qualities of fit
|
|
self.fit_quality_dict = {'fastest_lowest_quality': 0.1,
|
|
'medium_speed_and_quality': 0.01,
|
|
'slow_high_quality': 0.001,
|
|
'slowest_highest_quality': 0.0001}
|
|
|
|
self.data_to_use_dict = {
|
|
'use_very_little_data': ca.use_very_little_data,
|
|
'use_all_aruco_and_some_accel': ca.use_all_aruco_and_some_accel,
|
|
'use_all_data': ca.use_all_data
|
|
}
|
|
|
|
|
|
def main(self, use_check_calibration_data):
|
|
|
|
rospy.init_node('process_head_calibration_data')
|
|
self.node_name = rospy.get_name()
|
|
rospy.loginfo("{0} started".format(self.node_name))
|
|
|
|
self.calibration_directory = rospy.get_param('~calibration_directory')
|
|
rospy.loginfo('Using the following directory for calibration files: {0}'.format(self.calibration_directory))
|
|
|
|
# load parameters for what data to fit and how well to fit it
|
|
head_calibration_options = rospy.get_param('/head_calibration_options')
|
|
self.data_to_use = head_calibration_options['data_to_use']
|
|
self.fit_quality = head_calibration_options['fit_quality']
|
|
if self.data_to_use not in self.data_to_use_dict.keys():
|
|
rospy.logerr('Unrecognized option: data_to_use = {0}, valid options are {1}'.format(self.data_to_use, self.data_to_use_dict.keys()))
|
|
if self.fit_quality not in self.fit_quality_dict.keys():
|
|
rospy.logerr('Unrecognized option: fit_quality = {0}, valid options are {1}'.format(self.data_to_use, self.fit_quality_dict.keys()))
|
|
rospy.loginfo('data_to_use = {0}'.format(self.data_to_use))
|
|
rospy.loginfo('fit_quality = {0}'.format(self.fit_quality))
|
|
|
|
self.cma_tolfun = self.fit_quality_dict[self.fit_quality]
|
|
self.sample_selector_func = self.data_to_use_dict[self.data_to_use]
|
|
|
|
# load default tilt backlash transition angle
|
|
self.uncalibrated_controller_calibration_filename = rospy.get_param('~uncalibrated_controller_calibration_filename')
|
|
rospy.loginfo('Loading factory default tilt backlash transition angle from the YAML file named {0}'.format(self.uncalibrated_controller_calibration_filename))
|
|
fid = open(self.uncalibrated_controller_calibration_filename, 'r')
|
|
default_controller_parameters = yaml.load(fid)
|
|
fid.close()
|
|
tilt_angle_backlash_transition_rad = default_controller_parameters['tilt_angle_backlash_transition']
|
|
deg_per_rad = 180.0/math.pi
|
|
rospy.loginfo('self.tilt_angle_backlash_transition_rad in degrees = {0}'.format(tilt_angle_backlash_transition_rad * deg_per_rad))
|
|
|
|
self.uncalibrated_urdf_filename = rospy.get_param('~uncalibrated_urdf_filename')
|
|
rospy.loginfo('The uncalibrated URDF filename: {0}'.format(self.uncalibrated_urdf_filename))
|
|
|
|
if self.load_most_recent_opt_results or (self.opt_results_file_to_load is not None):
|
|
if self.load_most_recent_opt_results:
|
|
filenames = glob.glob(self.calibration_directory + 'head_calibration_result' + '_*[0-9].yaml')
|
|
filenames.sort()
|
|
filename = filenames[-1]
|
|
print('Loading most recent CMA-ES result from a YAML file named ' + filename)
|
|
else:
|
|
filename = self.opt_results_file_to_load
|
|
print('Loading CMA-ES result from a YAML file named ' + filename)
|
|
fid = open(filename, 'r')
|
|
cma_result = yaml.load(fid)
|
|
fid.close()
|
|
|
|
show_data_used_during_optimization = True
|
|
if show_data_used_during_optimization:
|
|
# attempt to visualize with the same data that was used during the optimization
|
|
self.data_to_use = cma_result.get('data_to_use', 'use_all_data')
|
|
else:
|
|
# show all data
|
|
self.data_to_use = 'use_all_data'
|
|
|
|
self.sample_selector_func = self.data_to_use_dict[self.data_to_use]
|
|
|
|
calibration_options = cma_result.get('calibration_options', {})
|
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fit_parameters = np.array(cma_result['best_parameters'])
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self.calibrator = HeadCalibrator(self.uncalibrated_urdf_filename, self.calibration_directory, self.sample_selector_func, calibration_options, self.visualize, tilt_angle_backlash_transition_rad)
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if self.visualize_only:
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print('Loading the most recent data file.')
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self.calibrator.load_data(fit_parameters, use_check_calibration_data=use_check_calibration_data)
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print('Visualizing how well the model fits the data.')
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print('Wait to make sure that RViz has time to load.')
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rospy.sleep(5.0)
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self.calibrator.visualize_fit(fit_parameters)
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else:
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time_string = ca.create_time_string()
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print('Updating the URDF with the parameters previously optimized with CMA-ES.')
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self.calibrator.update_urdf(fit_parameters)
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print('Saving the updated URDF file.')
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self.calibrator.save_urdf_file(time_string)
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print('Saving the updated controller calibration file.')
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self.calibrator.save_controller_calibration_file(time_string)
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else:
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start_time = time.time()
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calibration_options = {'calibrate_lift': True,
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'calibrate_arm': True,
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'calibrate_controller_offsets': True,
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'calibrate_pan_backlash': True,
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'calibrate_tilt_backlash': True,
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'calibrate_arm_backlash': True}
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|
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self.calibrator = HeadCalibrator(self.uncalibrated_urdf_filename, self.calibration_directory, self.sample_selector_func, calibration_options, self.visualize, tilt_angle_backlash_transition_rad)
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parameter_names = self.calibrator.get_names_of_parameters_to_fit()
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|
|
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#"incumbent solution"
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all_options = cma.CMAOptions()
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#"termination criterion: tolerance in function value"
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options = {'tolfun': self.cma_tolfun}
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|
|
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num_parameters = len(parameter_names)
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initial_solution = num_parameters * [0.0]
|
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self.calibrator.load_data(initial_solution)
|
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initial_standard_deviation = 0.1
|
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es = cma.CMAEvolutionStrategy(initial_solution, initial_standard_deviation, options)
|
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es.optimize(self.calibrator.calculate_error)
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print
|
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print('Optimization complete.')
|
|
print
|
|
es.result_pretty()
|
|
|
|
end_time = time.time()
|
|
calibration_time_in_minutes = (end_time - start_time)/60.0
|
|
print('Minutes spent calibrating: ', calibration_time_in_minutes)
|
|
|
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# A results tuple from CMAEvolutionStrategy property result.
|
|
|
|
# This tuple contains in the given position and as attribute
|
|
|
|
# 0 xbest best solution evaluated
|
|
# 1 fbest objective function value of best solution
|
|
# 2 evals_best evaluation count when xbest was evaluated
|
|
# 3 evaluations evaluations overall done
|
|
# 4 iterations
|
|
# 5 xfavorite distribution mean in "phenotype" space, to be considered as current best estimate of the optimum
|
|
# 6 stds effective standard deviations, can be used to compute a lower bound on the expected coordinate-wise distance to the true optimum, which is (very) approximately stds[i] * dimension**0.5 / min(mueff, dimension) / 1.5 / 5 ~ std_i * dimension**0.5 / min(popsize / 2, dimension) / 5, where dimension = CMAEvolutionStrategy.N and mueff = CMAEvolutionStrategy.sp.weights.mueff ~ 0.3 * popsize.
|
|
|
|
# Get best error terms
|
|
best_parameters = es.result[0]
|
|
best_total_error, best_error_terms = self.calibrator.calculate_error(best_parameters, output_error_terms=True)
|
|
for key in best_error_terms:
|
|
best_error_terms[key] = float(best_error_terms[key])
|
|
|
|
# Convert from Numpy arrays to human-readable lists
|
|
no_numpy_cma_result = []
|
|
for entry in es.result:
|
|
if "tolist" in dir(entry):
|
|
entry = entry.tolist()
|
|
no_numpy_cma_result.append(entry)
|
|
|
|
# Create a dictionary with the parameters to improve human
|
|
# readability
|
|
best_parameter_dict = {}
|
|
for name, value in zip(parameter_names, no_numpy_cma_result[0]):
|
|
best_parameter_dict[name] = value
|
|
|
|
cma_result = {
|
|
'calibration_time_in_minutes': calibration_time_in_minutes,
|
|
'initial_solution': initial_solution,
|
|
'initial_standard_deviation': initial_standard_deviation,
|
|
'options': options,
|
|
'calibration_options': self.calibrator.get_calibration_options(),
|
|
'parameter_names': parameter_names,
|
|
'best_parameters': no_numpy_cma_result[0],
|
|
'best_parameter_dict': best_parameter_dict,
|
|
'best_parameters_error': no_numpy_cma_result[1],
|
|
'best_parameters_error_terms': best_error_terms,
|
|
'num_evals_to_find_best': no_numpy_cma_result[2],
|
|
'num_evals_total': no_numpy_cma_result[3],
|
|
'cma_iterations': no_numpy_cma_result[4],
|
|
'cma_parameter_means': no_numpy_cma_result[5],
|
|
'cma_parameter_stddevs': no_numpy_cma_result[6],
|
|
'fit_quality_dict': self.fit_quality_dict,
|
|
'fit_quality': self.fit_quality,
|
|
'data_to_use': self.data_to_use,
|
|
'observations_used_for_fit': self.calibrator.observations_used_for_fit,
|
|
'calibration_data_filename': self.calibrator.head_calibration_data_filename,
|
|
'error_weights': self.calibrator.error_weights
|
|
}
|
|
|
|
time_string = ca.create_time_string()
|
|
|
|
filename = self.calibration_directory + 'head_calibration_result_' + time_string + '.yaml'
|
|
if not self.visualize_only:
|
|
print()
|
|
print('********************************************************')
|
|
filename = self.calibration_directory + 'head_calibration_result_' + time_string + '.yaml'
|
|
print('Saving CMA-ES result to a YAML file named ', filename)
|
|
fid = open(filename, 'w')
|
|
yaml.dump(cma_result, fid)
|
|
fid.close()
|
|
print('Finished saving.')
|
|
|
|
fit_parameters = cma_result['best_parameters']
|
|
self.calibrator.update_urdf(fit_parameters)
|
|
|
|
self.calibrator.save_urdf_file(time_string)
|
|
self.calibrator.save_controller_calibration_file(time_string)
|
|
else:
|
|
print()
|
|
print('********************************************************')
|
|
print('Not saving due to visualize only mode')
|
|
print()
|
|
print('Would have saved the CMA-ES result to a YAML file named ', filename)
|
|
print()
|
|
print('The following cma_result would have been saved:')
|
|
print(cma_result)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = ap.ArgumentParser(description='Process head calibration data and work with resulting files.')
|
|
parser.add_argument('--load', action='store', help='Do not perform an optimization and instead load the specified file, which should contain CMA-ES optimization results.', default=None)
|
|
parser.add_argument('--load_prev', action='store_true', help='Do not perform an optimization and instead load the most recent CMA-ES optimization results.')
|
|
parser.add_argument('--only_vis', action='store_true', help='Only visualize the fit of the CMA-ES optimization results. This does not save any results.')
|
|
parser.add_argument('--no_vis', action='store_true', help='Do not calculate or publish any visualizations. This results in faster fitting.')
|
|
parser.add_argument('--check', action='store_true', help='Use data collected to check the current calibration, instead of data collected to fit a new calibration.')
|
|
|
|
args, unknown = parser.parse_known_args()
|
|
|
|
opt_results_file_to_load = args.load
|
|
load_most_recent_opt_results = args.load_prev
|
|
visualize_only = args.only_vis
|
|
turn_on_visualization = not args.no_vis
|
|
use_check_calibration_data = args.check
|
|
|
|
node = ProcessHeadCalibrationDataNode(opt_results_file_to_load = opt_results_file_to_load, load_most_recent_opt_results = load_most_recent_opt_results, visualize_only = visualize_only, visualize=turn_on_visualization)
|
|
node.main(use_check_calibration_data)
|