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373 lines
14 KiB
373 lines
14 KiB
#!/usr/bin/env python
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from __future__ import print_function
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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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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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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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from hello_helpers.hello_misc import *
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import hello_helpers.hello_ros_viz as hr
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def use_all_data(sample_number, sample):
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# use all samples
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return True
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def use_very_little_data(sample_number, sample):
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# use every twentieth sample
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if (sample_number % 20) == 0:
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return True
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else:
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return False
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def use_all_aruco_and_some_accel(sample_number, sample):
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# Use all samples with one or more ArUco marker observations, and
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# a seventh of the remaining samples.
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c = sample['camera_measurements']
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wrist_inside_marker_detected = (c['wrist_inside_marker_pose'] != None)
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wrist_top_marker_detected = (c['wrist_top_marker_pose'] != None)
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base_left_marker_detected = (c['base_left_marker_pose'] != None)
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base_right_marker_detected = (c['base_right_marker_pose'] != None)
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# If an ArUco marker is detected, use the sample
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if wrist_top_marker_detected or wrist_inside_marker_detected or base_left_marker_detected or base_right_marker_detected:
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return True
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# For all other samples (accelerometer samples) use only some of the samples.
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if (sample_number % 7) == 0:
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return True
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return False
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def soft_constraint_error(x, thresh, scale):
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# Returns a soft constraint error that is zero below the
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# constraint and increases linearly after the constraint.
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if x > thresh:
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return scale * (x - thresh)
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else:
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return 0.0
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def affine_matrix_difference(t1, t2, size=4):
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error = 0.0
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for i in range(size):
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for j in range(size):
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error += abs(t1[i,j] - t2[i,j])
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return error
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def rot_to_axes(r):
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x_axis = np.reshape(r[:3,0], (3,1))
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y_axis = np.reshape(r[:3,1], (3,1))
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z_axis = np.reshape(r[:3,2], (3,1))
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return [x_axis, y_axis, z_axis]
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def norm_axes(axes):
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x_axis, y_axis, z_axis = axes
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x_axis = x_axis / np.linalg.norm(x_axis)
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y_axis = y_axis / np.linalg.norm(y_axis)
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z_axis = z_axis / np.linalg.norm(z_axis)
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return [x_axis, y_axis, z_axis]
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def quat_to_rotated_axes(rot_mat, q):
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r = Rotation.from_quat([q.x, q.y, q.z, q.w]).as_dcm()
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rotated_r = np.matmul(rot_mat, r)
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return rot_to_axes(rotated_r)
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def axis_error(axis, axis_target):
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# dot product comparison: 1.0 is best case and -1.0 is worst case
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error = np.dot(axis_target.transpose(), axis)
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# linear transform: 0.0 is best case and 1.0 is worst case
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return (1.0 - error) / 2.0
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def axes_error(axes, axes_target):
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# 0.0 is the best case and 1.0 is the worst case
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errors = np.array([axis_error(axis, axis_target) for axis, axis_target in zip(axes, axes_target)])
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return np.sum(errors)/3.0
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class Joint:
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# wrapper for a URDF joint
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def __init__(self, urdf_joint):
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self.joint = urdf_joint
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self.is_joint = True
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self.is_link = False
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def get_affine_matrix(self, value):
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axis = self.joint.axis
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rpy = self.joint.origin.rpy
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xyz = self.joint.origin.xyz
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affine_matrix = np.identity(4)
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affine_matrix[:3, 3] = xyz
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affine_matrix[:3,:3] = Rotation.from_euler('xyz', rpy).as_dcm()
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if self.joint.type == 'revolute':
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axis_affine_matrix = np.identity(4)
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rotation_vector = value * (np.array(axis)/np.linalg.norm(axis))
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axis_affine_matrix[:3,:3] = Rotation.from_rotvec(rotation_vector).as_dcm()
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affine_matrix = np.matmul(affine_matrix, axis_affine_matrix)
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elif self.joint.type == 'prismatic':
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axis_affine_matrix = np.identity(4)
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axis_affine_matrix[:3, 3] = value * (np.array(axis)/np.linalg.norm(axis))
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affine_matrix = np.matmul(affine_matrix, axis_affine_matrix)
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elif self.joint.type == 'fixed':
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affine_matrix = affine_matrix
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else:
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print('ERROR: unrecognized joint type = ', self.joint_type)
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exit()
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return affine_matrix
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def __str__(self):
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return self.joint.__str__()
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class Link:
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# wrapper for a URDF link
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def __init__(self, urdf_link):
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self.link = urdf_link
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self.is_joint = False
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self.is_link = True
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def __str__(self):
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return self.link.__str__()
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class Chain:
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# wrapper for a URDF chain
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def __init__(self, urdf, start_name, end_name):
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self.urdf = urdf
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self.chain_names = self.urdf.get_chain(start_name, end_name)
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self.chain = []
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for name in self.chain_names:
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is_joint = self.urdf.joint_map.has_key(name)
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is_link = self.urdf.link_map.has_key(name)
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if is_joint and is_link:
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print('ERROR: a joint and a link have the same name. This is not supported.')
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exit()
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if is_joint:
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self.chain.append(Joint(self.urdf.joint_map[name]))
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elif is_link:
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self.chain.append(Link(self.urdf.link_map[name]))
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else:
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print('ERROR: no joint or link was found with the name returned by get_chain')
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exit()
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def get_joint_by_name(self, name):
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for e in self.chain:
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if e.is_joint:
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if e.joint.name == name:
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return e.joint
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return None
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def get_affine_matrix(self, joint_value_dict):
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affine_matrix = np.identity(4)
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for e in self.chain:
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if e.is_joint:
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value = joint_value_dict.get(e.joint.name, 0.0)
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affine_matrix = np.matmul(affine_matrix, e.get_affine_matrix(value))
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return affine_matrix
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class ArucoError:
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# This object handles error calculations for a single ArUco
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# marker. For an example of its use, please see
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# process_head_calibration_data.
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def __init__(self, name, location, aruco_link, urdf, meters_per_deg, rgba):
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self.aruco_link = aruco_link
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# This creates a wrapper around a mutable URDF object that is
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# shared across multiple chains. When calibrating the URDF,
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# the URDF and this chain are updated outside of this
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# ArucoError object.
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self.marker_frame = '/base_link'
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self.aruco_chain = Chain(urdf, 'base_link', self.aruco_link)
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self.rgba = rgba
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self.name = name
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self.location = location
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self.detected = False
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self.observed_aruco_pose = None
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self.joint_values = None
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self.meters_per_deg = meters_per_deg
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self.number_of_observations = None
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def reset_observation_count(self):
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# Set observation count to zero.
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self.number_of_observations = 0
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def increment_observation_count(self, sample):
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# Increments the observation count if the provided sample
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# includes an observation of this ArUco marker.
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c = sample['camera_measurements']
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if (c[self.name + '_marker_pose'] != None):
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self.number_of_observations += 1
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def update(self, sample, marker_time, unused):
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# Use the sample to update the ArUco marker's observed pose
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# and the corresponding robot joint configuration.
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camera_measurements = sample['camera_measurements']
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self.detected = (camera_measurements.get(self.name + '_marker_pose') != None)
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if self.detected is None:
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self.detected = False
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self.observed_aruco_pose = None
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else:
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self.observed_aruco_pose = camera_measurements[self.name + '_marker_pose']
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self.joint_values = sample['joints']
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self.marker_time = marker_time
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def get_target_ros_markers(self, sample, marker_id, marker_time, rgba, unused):
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# If this ArUco marker was observed in the sample, create ROS
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# markers for visualization of this ArUco marker as predicted
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# by the current URDF using the robot's configuration for the
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# sample.
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ros_markers = []
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camera_measurements = sample['camera_measurements']
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detected = (camera_measurements.get(self.name + '_marker_pose') != None)
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if (detected is None) or (not detected):
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return [], marker_id
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joint_values = sample['joints']
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marker_time = marker_time
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target_transform = self.aruco_chain.get_affine_matrix(joint_values)
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target_xyz = target_transform[:3,3]
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target_axes = rot_to_axes(target_transform[:3,:3])
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xyz = target_xyz
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marker = hr.create_sphere_marker(xyz, marker_id, self.marker_frame, marker_time, rgba)
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marker_id += 1
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ros_markers.append(marker)
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z_axis = target_axes[2]
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marker = hr.create_axis_marker(xyz, z_axis, marker_id, self.marker_frame, marker_time, rgba)
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marker_id += 1
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ros_markers.append(marker)
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# A list of ROS markers for visualization, and an ID number
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# for the last ROS marker generated.
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return ros_markers, marker_id
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def error(self, camera_transform, fit_z, fit_orientation, marker_id, visualize=True, visualize_targets=False):
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# Calculate the error between the observed ArUco marker pose
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# and the ArUco marker pose predicted by the current URDF (the
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# target pose). The update method must be called before
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# calling this method. This method should not be called if the
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# ArUco marker was not observed. For example, if self.detected
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# is False, then self.observed_aruco_pose will be None and
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# this method will fail.
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# Initialize the position and orientation error. If
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# fit_orientation is False, the orientation error will not be
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# updated.
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error = {'pos': 0.0,
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'orient': 0.0}
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# Find the ArUco marker pose predicted by the current URDF
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# (the target pose).
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target_transform = self.aruco_chain.get_affine_matrix(self.joint_values)
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target_xyz = target_transform[:3,3]
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target_axes = rot_to_axes(target_transform[:3,:3])
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# Transform the camera observation of the ArUco marker into
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# the world coordinate system using the provided camera
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# transform.
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p = self.observed_aruco_pose.position
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observed_xyz = np.dot(camera_transform, np.array([p.x, p.y, p.z, 1.0]))[:3]
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if fit_orientation:
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q = self.observed_aruco_pose.orientation
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observed_axes = quat_to_rotated_axes(camera_transform[:3,:3], q)
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# With respect to the world frame, calculate errors by
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# comparing the transformed camera observation of the ArUco
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# marker with the current URDF's ArUco marker prediction.
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if not fit_z:
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# Only fit planar direction to the marker
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# position due to downward deflection of the
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# telescoping arm.
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vec1 = observed_xyz[:2]
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vec2 = target_xyz[:2]
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vec1 = vec1 / np.linalg.norm(vec1)
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vec2 = vec2 / np.linalg.norm(vec2)
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error['pos'] += (self.meters_per_deg * axis_error(vec1, vec2)) / self.number_of_observations
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else:
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if fit_orientation:
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# Calculate the position and orientation errors.
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error['pos'] += np.linalg.norm(observed_xyz - target_xyz) / self.number_of_observations
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# 0.0 is the best case and 1.0 is the worst case
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error['orient'] += (self.meters_per_deg * axes_error(observed_axes, target_axes)) / self.number_of_observations
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else:
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# Only calculate the position error.
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error['pos'] += np.linalg.norm(observed_xyz - target_xyz) / self.number_of_observations
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# If requested, generate ROS markers to visualize the pose of
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# the ArUco observation and/or the pose of the ArUco marker
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# predicted by the current URDF.
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ros_markers = []
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if visualize:
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xyz = observed_xyz
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marker = hr.create_sphere_marker(xyz, marker_id, self.marker_frame, self.marker_time, self.rgba)
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marker_id += 1
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ros_markers.append(marker)
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if fit_orientation:
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z_axis = observed_axes[2]
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marker = hr.create_axis_marker(xyz, z_axis, marker_id, self.marker_frame, self.marker_time, self.rgba)
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marker_id += 1
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ros_markers.append(marker)
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if visualize_targets:
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target_rgba = [1.0, 1.0, 1.0, 1.0]
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xyz = target_xyz
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marker = hr.create_sphere_marker(xyz, marker_id, self.marker_frame, self.marker_time, target_rgba)
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marker_id += 1
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ros_markers.append(marker)
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if fit_orientation:
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z_axis = target_axes[2]
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marker = hr.create_axis_marker(xyz, z_axis, marker_id, self.marker_frame, self.marker_time, target_rgba)
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marker_id += 1
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ros_markers.append(marker)
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# Returns the error dictionary, a list of ROS markers for
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# visualization, and an ID number for the last ROS marker
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# generated.
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return error, ros_markers, marker_id
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