single_transform.py
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00032 
00033 import numpy
00034 from numpy import matrix, vsplit, sin, cos, reshape, zeros, pi
00035 import rospy
00036 
00037 import tf.transformations as transformations
00038 import yaml, math
00039 
00040 # This represents the transform for a single joint in the URDF
00041 # parameters are x,y,z and rotation as angle-axis
00042 
00043 param_names = ['x','y','z','a','b','c']
00044 
00045 class SingleTransform:
00046     def __init__(self, config = [0, 0, 0, 0, 0, 0], name=""):
00047         self._name = name
00048         eval_config = [eval(str(x)) for x in config]
00049         self._config = reshape(matrix(eval_config, float), (-1,1))
00050 
00051         rospy.logdebug("Initializing single transform %s with params [%s]", name, ", ".join(["% 2.4f" % x for x in eval_config]))
00052         self.inflate(self._config)
00053 
00054     def calc_free(self, free_config):
00055         return [x == 1 for x in free_config]
00056 
00057     def params_to_config(self, param_vec):
00058         return param_vec.T.tolist()[0]
00059 
00060     # Convert column vector of params into config
00061     def inflate(self, p):
00062         self._config = p.copy()  # Once we can back compute p from T, we don't need _config
00063 
00064         # Init output matrix
00065         T = matrix( zeros((4,4,), float))
00066         T[3,3] = 1.0
00067         
00068         # Copy position into matrix
00069         T[0:3,3] = p[0:3,0]
00070         
00071         # Renormalize the rotation axis to be unit length
00072         U,S,Vt = numpy.linalg.svd(p[3:6,0])
00073         a = U[:,0]
00074         rot_angle = S[0]*Vt[0,0]
00075         
00076         # Build rotation matrix
00077         c = cos(rot_angle)
00078         s = sin(rot_angle)
00079         R = matrix( [ [   a[0,0]**2+(1-a[0,0]**2)*c, a[0,0]*a[1,0]*(1-c)-a[2,0]*s, a[0,0]*a[2,0]*(1-c)+a[1,0]*s],
00080                       [a[0,0]*a[1,0]*(1-c)+a[2,0]*s,    a[1,0]**2+(1-a[1,0]**2)*c, a[1,0]*a[2,0]*(1-c)-a[0,0]*s],
00081                       [a[0,0]*a[2,0]*(1-c)-a[1,0]*s, a[1,0]*a[2,0]*(1-c)+a[0,0]*s,    a[2,0]**2+(1-a[2,0]**2)*c] ] )
00082 
00083         T[0:3,0:3] = R
00084         self.transform = T
00085 
00086     # Take transform, and convert into 6 param vector
00087     def deflate(self):
00088         # todo: This currently a hacky stub. To be correct, this should infer the parameter vector from the 4x4 transform
00089         return self._config
00090 
00091     # Returns # of params needed for inflation & deflation
00092     def get_length(self):
00093         return len(param_names)
00094 
00095 
00096 # Convert from rotation-axis-with-angle-as-magnitude representation to Euler RPY
00097 def angle_axis_to_RPY(vec):
00098     angle = math.sqrt(sum([vec[i]**2 for i in range(3)]))
00099     hsa = math.sin(angle/2.)
00100     if epsEq(angle, 0):
00101         return (0.,0.,0.)
00102     quat = [vec[0]/angle*hsa, vec[1]/angle*hsa, vec[2]/angle*hsa, math.cos(angle/2.)]
00103     rpy = quat_to_rpy(quat)
00104     return rpy
00105 
00106 # Convert from Euler RPY to rotation-axis-with-angle-as-magnitude
00107 def RPY_to_angle_axis(vec):
00108     if epsEq(vec[0], 0) and epsEq(vec[1], 0) and epsEq(vec[2], 0):
00109         return [0.0, 0.0, 0.0]
00110     quat = rpy_to_quat(vec)
00111     angle = math.acos(quat[3])*2.0
00112     hsa = math.sin(angle/2.)
00113     axis = [quat[0]/hsa*angle, quat[1]/hsa*angle, quat[2]/hsa*angle]
00114     return axis
00115     
00116 def rpy_to_quat(rpy):
00117     return transformations.quaternion_from_euler(rpy[0], rpy[1], rpy[2], 'sxyz')
00118 
00119 def quat_to_rpy(q):
00120     rpy = transformations.euler_from_quaternion(q, 'sxyz')
00121     return rpy
00122 
00123 #return 1 if value1 and value2 are within eps of each other, 0 otherwise
00124 def epsEq(value1, value2, eps = 1e-10):
00125     if math.fabs(value1-value2) <= eps:
00126         return 1
00127     return 0
00128 
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calibration_estimation
Author(s): Vijay Pradeep, Michael Ferguson
autogenerated on Thu Aug 15 2013 10:15:40