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ekf.py
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# Position estimation
# @author: harold.murcia
# Marzo 16 /19
import rospy
import time
import numpy as np
from std_msgs.msg import String
from sensor_msgs.msg import Imu
from geometry_msgs.msg import Vector3Stamped
import matplotlib.pyplot as plt
import tf
from geometry_msgs.msg import Quaternion, Twist
from nav_msgs.msg import Odometry
from math import pi, cos, sin
import diagnostic_msgs
import diagnostic_updater
class NodoPos(object): # Crea clase
def __init__(self):
self.ekf_pub = rospy.Publisher('/ekf', Odometry, queue_size=10)
self.enc_start_flag = 0 #una variable con memoria de theta
self.enc_x=0
self.enc_y=0
self.enc_yaw=0
self.enc_N=100.0
self.enc_angle_l=0
self.enc_angle_l_1=0
self.enc_angle_r=0
self.enc_angle_r_1=0
self.enc_inc_r =0
self.enco_inc_l=0
self.enc_radio = 7.2/100.0
self.enc_yo = 0.72
self.enc_PPR = 6533.0
self.enc_wr =0
self.enc_wl =0
self.theta_l=0
self.theta_r=0
self.enc_vr=0
self.enc_vl=0
self.enc_delta_s = 0
self.enc_delta_s_1 =0
self.enc_delta_yaw = 0
self.enc_Ts = 1/10.0
self.enc_tic = 0
self.enc_toc = 1/10.0
self.enc_X=[]
self.enc_Y=[]
self.enc_acum_yaw = []
self.enc_samples=0
self.enc_WL = []
self.enc_WR = []
# I M U
self.imu_samples = 0
self.imu_Ts = 1/100.0
self.imu_ax = 0
self.imu_ay = 0
self.imu_az = 0
self.imu_vx = 0
self.imu_vy = 0
self.imu_vz = 0
self.imu_x = 0
self.imu_y = 0
self.imu_z = 0
self.imu_mean_ax = -0.0628382861614
self.imu_mean_ay = -0.347958862782
self.imu_mean_az = 9.80008125305
self.imu_std_yaw = []
self.imu_std_ax = 0
self.imu_std_ay = 0
self.imu_std_az = 0
self.imu_mean_yaw = []
self.imu_yaw = 0
self.imu_pitch = 0
self.imu_roll = 0
self.imu_acum_X = []
self.imu_acum_Y = []
self.imu_acum_Z = []
self.imu_acum_yaw = []
self.mag_yaw = 0
self.mag_acum_yaw = []
self.mag_integral_yaw = 0
self.mag_aux = []
#
self.enc_integral_yaw = 0
self.enc_aux = []
self.kalman_no_filtered_yaw = []
# K A L M A N
self.kalman_P = 1.0*np.identity(3)
self.kalman_Q = 1.0*np.matrix([[1.0, 0, 0],[0, 1.0, 0],[0,0,0.1]])
self.kalman_G = np.matrix([[0, 0],[0,0], [0,0]])
self.kalman_R = 0.0265*1.0
self.kalman_Xk = np.matrix([[0],[0],[100.0]])
self.kalman_filtered_yaw = []
# INIT
#rospy.loginfo("Starting node")
rospy.Subscriber('/encoders', String, self.Enc_Kalman) #10 Hz
rospy.Subscriber('/imu_data', Imu, self.calIMU) #100 Hz
rospy.Subscriber('/mti/sensor/magnetic',Vector3Stamped, self.magIMU) #100 Hz
rospy.spin()
def Enc_Kalman(self,data):
self.enc_toc= rospy.get_time()
self.enc_Ts = (self.enc_toc - self.enc_tic)
self.enc_tic = rospy.get_time()
self.enc_samples+=1
data=str(data)
data = data.split(",")
angle_l = data[0].split(":")
self.enc_angle_l = float(angle_l[2])
angle_r = data[1].split(":")
angle_r = angle_r[1].strip('"')
self.enc_angle_r = float(angle_r)
if self.enc_samples == 1:
self.enc_angle_l_1 = self.enc_angle_l
self.enc_angle_l_1 = self.enc_angle_l
self.enc_inc_l = self.enc_angle_l-self.enc_angle_l_1
self.enc_inc_r = self.enc_angle_r-self.enc_angle_r_1
self.enc_angle_l_1 = self.enc_angle_l
self.enc_angle_r_1 = self.enc_angle_r
self.enc_wl = (2.0*np.pi/self.enc_PPR)*(self.enc_inc_l/self.enc_Ts)
self.enc_wr = (2.0*np.pi/self.enc_PPR)*(self.enc_inc_r/self.enc_Ts)
self.enc_vr = self.enc_radio*self.enc_wr
self.enc_vl = self.enc_radio*self.enc_wl
self.enc_VX = (self.enc_vr+self.enc_vl)/2.0
self.theta_l = self.theta_l + (2.0*np.pi/self.enc_PPR)*self.enc_inc_l
self.theta_r = self.theta_r + (2.0*np.pi/self.enc_PPR)*self.enc_inc_r
A = np.matrix([[self.enc_radio, self.enc_radio],[self.enc_radio/self.enc_yo, -self.enc_radio/self.enc_yo]])
U = np.matrix([[self.enc_wr],[self.enc_wl]])
X = 0.5*A*U
self.enc_delta_s_1 = self.enc_delta_s
self.enc_delta_s = X[0]*self.enc_Ts
self.enc_delta_yaw = X[1]*self.enc_Ts
if self.enc_samples < 50 and self.enc_samples>10: #:520 and self.enc_samples>100
self.imu_acum_yaw.append(self.imu_yaw)
self.mag_acum_yaw.append(self.mag_yaw)
#rospy.loginfo("Samples " + str(self.enc_samples))
if self.enc_samples > 50: # 520:
self.enc_WR.append(self.enc_wr)
self.enc_WL.append(self.enc_wl)
media = np.array( self.imu_acum_yaw)
media_mag = np.array(self.mag_acum_yaw)
self.kalman_no_filtered_yaw.append( float(self.imu_yaw-media.mean()) )
self.enc_integral_yaw += self.enc_delta_yaw
#self.mag_integral_yaw += self.mag_yaw
self.mag_aux.append(float( self.mag_yaw -media_mag.mean() ))
self.enc_aux.append(float( self.enc_integral_yaw ))
# K A L M A N F i l t e r
# Model update
kalman_delta_x = float( self.enc_delta_s*( np.cos( self.kalman_Xk[2] +self.enc_delta_yaw/2.0 )*np.cos(self.imu_pitch) ))
kalman_delta_y = float( self.enc_delta_s*( np.sin( self.kalman_Xk[2] +self.enc_delta_yaw/2.0 )*np.cos(self.imu_pitch) ))
kalman_delta_yaw = float( self.enc_delta_yaw*( np.cos(self.imu_roll)/(np.cos(self.imu_pitch)+1e-6) ))
#print "delta: "+ str(np.matrix([[kalman_delta_x],[kalman_delta_y],[kalman_delta_yaw]]))
self.kalman_Xk = self.kalman_Xk + np.matrix([[kalman_delta_x],[kalman_delta_y],[kalman_delta_yaw]])
#self.kalman_filtered_yaw.append( float( self.kalman_Xk[2]) )
# Jacobian computation
kalman_F = np.matrix([[1.0, 0.0, -self.enc_delta_s*np.sin( self.kalman_Xk[2] +self.enc_delta_yaw/2.0)*np.cos(self.imu_pitch)], [0.0, 1.0, self.enc_delta_s*np.cos( self.kalman_Xk[2] +self.enc_delta_yaw/2.0)*np.cos(self.imu_pitch)], [0.0, 0.0, 1.0*np.cos(self.imu_roll)/(np.cos(self.imu_pitch)+1e-6)]])
kalman_H = np.matrix([0.0,0.0,1.0])
# Error covariance
#self.kalman_G = np.matrix([[self.enc_Ts*np.cos( self.kalman_Xk[2] +self.enc_delta_yaw/2.0 ), 0],[self.enc_Ts*np.sin( self.kalman_Xk[2] +self.enc_delta_yaw/2.0 ),0], [0,self.enc_Ts]])
self.kalman_P = np.dot(np.dot(kalman_F, self.kalman_P), kalman_F.T) +self.kalman_Q
# Kalman filter Gain update
kalman_S = np.linalg.inv( self.kalman_R + np.dot( np.dot(kalman_H,self.kalman_P) ,kalman_H.T) )
kalman_K = np.dot( np.dot(self.kalman_P,kalman_H.T) , kalman_S)
# Kalman measurement update
kalman_Yk = np.dot(kalman_H, self.kalman_Xk)
kalman_Zk = float( self.imu_yaw - media.mean() )
self.kalman_Xk = self.kalman_Xk + np.dot(kalman_K, (kalman_Zk-kalman_Yk))
#print str("KalmanK add ")+str(np.dot(kalman_K, (kalman_Zk-kalman_Yk)))
#print "Z: "+str(kalman_Zk)+"\t"+"Y: "+str(kalman_Yk)+"\t"+"KalmanK: "+str(kalman_K)
self.kalman_P = np.dot( (np.identity(3) - np.dot(kalman_K,kalman_H)) ,self.kalman_P)
#self.enc_X.append( float( self.kalman_Xk[0]) )
#self.enc_Y.append( float( self.kalman_Xk[1]) )
self.enc_x = float( self.kalman_Xk[0])
self.enc_y = float( self.kalman_Xk[1])
self.kalman_filtered_yaw = float( self.kalman_Xk[2])
self.publish_odom(self.enc_x, self.enc_y, self.kalman_filtered_yaw, self.enc_VX, self.enc_delta_yaw)
def magIMU(self,data):
magx= np.array(data.vector.x)
magy= np.array(data.vector.y)
magz= np.array(data.vector.z)
mag_norm = np.sqrt(np.power(magx,2)+np.power(magy,2)+np.power(magz,2))
magx = magx / mag_norm
magy = magy / mag_norm
magz = magz / mag_norm
Yh = (magy * np.cos(self.imu_roll)) - (magz * np.sin(self.imu_roll))
Xh = (magx * np.cos(self.imu_pitch))+(magy * np.sin(self.imu_roll)*np.sin(self.imu_pitch)) + (magz * np.cos(self.imu_roll) * np.sin(self.imu_pitch))
#mag_yaw = np.arctan(np.array(magy),np.array(magx))
mag_yaw = np.arctan(-np.array(Yh), np.array(Xh))
self.mag_yaw = mag_yaw
#print str(mag_yaw)
def calIMU(self,data):
self.imu_samples +=1
qx = data.orientation.x
qy = data.orientation.y
qz = data.orientation.z
qw = data.orientation.w
q = np.array([qx,qy,qz,qw])
euler = tf.transformations.euler_from_quaternion(q)
self.imu_pitch = euler[0]
self.imu_roll = euler[1]
self.imu_yaw = euler[2]
#print str(euler[2])
#print str(self.imu_pitch) + "\t" + str(self.imu_roll)+"\t"+str(self.imu_yaw)
def publish_odom(self, cur_x, cur_y, cur_theta, vx, vth):
quat = tf.transformations.quaternion_from_euler(0, 0, cur_theta)
current_time = rospy.Time.now()
br = tf.TransformBroadcaster()
br.sendTransform((cur_x, cur_y, 0),
tf.transformations.quaternion_from_euler(0, 0, cur_theta),
current_time,
"base_link",
"odom")
odom = Odometry()
odom.header.stamp = current_time
odom.header.frame_id = 'odom'
odom.pose.pose.position.x = cur_x
odom.pose.pose.position.y = cur_y
odom.pose.pose.position.z = 0.0
odom.pose.pose.orientation = Quaternion(*quat)
odom.pose.covariance[0] = 0.01
odom.pose.covariance[7] = 0.01
odom.pose.covariance[14] = 99999
odom.pose.covariance[21] = 99999
odom.pose.covariance[28] = 99999
odom.pose.covariance[35] = 0.01
odom.child_frame_id = 'base_link'
odom.twist.twist.linear.x = vx
odom.twist.twist.linear.y = 0
odom.twist.twist.angular.z = vth
odom.twist.covariance = odom.pose.covariance
self.ekf_pub.publish(odom)
def main(self):
pub = rospy.Publisher('chatter', String, queue_size=10)
rospy.init_node('talker', anonymous=True)
rate = rospy.Rate(10) # 10hz
while not rospy.is_shutdown():
hello_str = "hello world %s" % rospy.get_time()
#rospy.loginfo(hello_str)
pub.publish(hello_str)
rate.sleep()
if __name__ == '__main__':
rospy.init_node("ekf_node")
cv = NodoPos()