291 lines
6.3 KiB
Python
291 lines
6.3 KiB
Python
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# -*- coding: utf-8 -*-
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"""
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Created on Sun May 24 08:39:36 2015
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@author: Roger
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"""
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#x = x x' y y' theta
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from math import cos, sin, sqrt, atan2
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import numpy as np
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from numpy import array, dot
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from numpy.linalg import pinv
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def print_x(x):
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print(x[0, 0], x[1, 0], np.degrees(x[2, 0]))
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def control_update(x, u, dt):
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""" x is [x, y, hdg], u is [vel, omega] """
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v = u[0]
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w = u[1]
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if w == 0:
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# approximate straight line with huge radius
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w = 1.e-30
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r = v/w # radius
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return x + np.array([[-r*sin(x[2]) + r*sin(x[2] + w*dt)],
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[ r*cos(x[2]) - r*cos(x[2] + w*dt)],
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[w*dt]])
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a1 = 0.001
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a2 = 0.001
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a3 = 0.001
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a4 = 0.001
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sigma_r = 0.1
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sigma_h = a_error = np.radians(1)
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sigma_s = 0.00001
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def normalize_angle(x, index):
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if x[index] > np.pi:
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x[index] -= 2*np.pi
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if x[index] < -np.pi:
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x[index] = 2*np.pi
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def ekfloc_predict(x, P, u, dt):
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h = x[2]
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v = u[0]
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w = u[1]
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if w == 0:
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# approximate straight line with huge radius
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w = 1.e-30
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r = v/w # radius
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sinh = sin(h)
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sinhwdt = sin(h + w*dt)
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cosh = cos(h)
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coshwdt = cos(h + w*dt)
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G = array(
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[[1, 0, -r*cosh + r*coshwdt],
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[0, 1, -r*sinh + r*sinhwdt],
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[0, 0, 1]])
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V = array(
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[[(-sinh + sinhwdt)/w, v*(sin(h)-sinhwdt)/(w**2) + v*coshwdt*dt/w],
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[(cosh - coshwdt)/w, -v*(cosh-coshwdt)/(w**2) + v*sinhwdt*dt/w],
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[0, dt]])
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# covariance of motion noise in control space
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M = array([[a1*v**2 + a2*w**2, 0],
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[0, a3*v**2 + a4*w**2]])
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x = x + array([[-r*sinh + r*sinhwdt],
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[r*cosh - r*coshwdt],
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[w*dt]])
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P = dot(G, P).dot(G.T) + dot(V, M).dot(V.T)
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return x, P
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def ekfloc(x, P, u, zs, c, m, dt):
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h = x[2]
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v = u[0]
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w = u[1]
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if w == 0:
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# approximate straight line with huge radius
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w = 1.e-30
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r = v/w # radius
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sinh = sin(h)
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sinhwdt = sin(h + w*dt)
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cosh = cos(h)
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coshwdt = cos(h + w*dt)
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F = array(
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[[1, 0, -r*cosh + r*coshwdt],
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[0, 1, -r*sinh + r*sinhwdt],
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[0, 0, 1]])
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V = array(
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[[(-sinh + sinhwdt)/w, v*(sin(h)-sinhwdt)/(w**2) + v*coshwdt*dt/w],
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[(cosh - coshwdt)/w, -v*(cosh-coshwdt)/(w**2) + v*sinhwdt*dt/w],
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[0, dt]])
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# covariance of motion noise in control space
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M = array([[a1*v**2 + a2*w**2, 0],
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[0, a3*v**2 + a4*w**2]])
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x = x + array([[-r*sinh + r*sinhwdt],
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[r*cosh - r*coshwdt],
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[w*dt]])
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P = dot(F, P).dot(F.T) + dot(V, M).dot(V.T)
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R = np.diag([sigma_r**2, sigma_h**2, sigma_s**2])
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for i, z in enumerate(zs):
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j = c[i]
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q = (m[j][0] - x[0, 0])**2 + (m[j][1] - x[1, 0])**2
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z_est = array([sqrt(q),
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atan2(m[j][1] - x[1, 0], m[j][0] - x[0, 0]) - x[2, 0],
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0])
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H = array(
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[[-(m[j, 0] - x[0, 0]) / sqrt(q), -(m[j, 1] - x[1, 0]) / sqrt(q), 0],
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[ (m[j, 1] - x[1, 0]) / q, -(m[j, 0] - x[0, 0]) / q, -1],
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[0, 0, 0]])
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S = dot(H, P).dot(H.T) + R
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#print('S', S)
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K = dot(P, H.T).dot(pinv(S))
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y = z - z_est
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normalize_angle(y, 1)
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y = array([y]).T
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#print('y', y)
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x = x + dot(K, y)
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I = np.eye(P.shape[0])
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I_KH = I - dot(K, H)
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#print('i', I_KH)
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P = dot(I_KH, P).dot(I_KH.T) + dot(K, R).dot(K.T)
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return x, P
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def ekfloc2(x, P, u, zs, c, m, dt):
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h = x[2]
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v = u[0]
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w = u[1]
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if w == 0:
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# approximate straight line with huge radius
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w = 1.e-30
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r = v/w # radius
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sinh = sin(h)
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sinhwdt = sin(h + w*dt)
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cosh = cos(h)
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coshwdt = cos(h + w*dt)
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F = array(
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[[1, 0, -r*cosh + r*coshwdt],
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[0, 1, -r*sinh + r*sinhwdt],
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[0, 0, 1]])
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V = array(
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[[(-sinh + sinhwdt)/w, v*(sin(h)-sinhwdt)/(w**2) + v*coshwdt*dt/w],
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[(cosh - coshwdt)/w, -v*(cosh-coshwdt)/(w**2) + v*sinhwdt*dt/w],
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[0, dt]])
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# covariance of motion noise in control space
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M = array([[a1*v**2 + a2*w**2, 0],
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[0, a3*v**2 + a4*w**2]])
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x = x + array([[-r*sinh + r*sinhwdt],
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[r*cosh - r*coshwdt],
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[w*dt]])
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P = dot(F, P).dot(F.T) + dot(V, M).dot(V.T)
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R = np.diag([sigma_r**2, sigma_h**2])
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for i, z in enumerate(zs):
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j = c[i]
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q = (m[j][0] - x[0, 0])**2 + (m[j][1] - x[1, 0])**2
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z_est = array([sqrt(q),
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atan2(m[j][1] - x[1, 0], m[j][0] - x[0, 0]) - x[2, 0]])
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H = array(
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[[-(m[j, 0] - x[0, 0]) / sqrt(q), -(m[j, 1] - x[1, 0]) / sqrt(q), 0],
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[ (m[j, 1] - x[1, 0]) / q, -(m[j, 0] - x[0, 0]) / q, -1]])
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S = dot(H, P).dot(H.T) + R
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#print('S', S)
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K = dot(P, H.T).dot(pinv(S))
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y = z - z_est
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normalize_angle(y, 1)
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y = array([y]).T
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#print('y', y)
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x = x + dot(K, y)
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print('x', x)
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I = np.eye(P.shape[0])
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I_KH = I - dot(K, H)
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P = dot(I_KH, P).dot(I_KH.T) + dot(K, R).dot(K.T)
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return x, P
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m = array([[5, 5],
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[7,6],
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[4, 8]])
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x = array([[2, 6, .3]]).T
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u = array([.5, .01])
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P = np.diag([1., 1., 1.])
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c = [0, 1, 2]
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import matplotlib.pyplot as plt
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from numpy.random import randn
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from filterpy.common import plot_covariance_ellipse
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from filterpy.kalman import KalmanFilter
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plt.figure()
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plt.plot(m[:, 0], m[:, 1], 'o')
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plt.plot(x[0], x[1], 'x', color='b', ms=20)
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xp = x.copy()
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dt = 0.1
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np.random.seed(1234)
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for i in range(1000):
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xp, _ = ekfloc_predict(xp, P, u, dt)
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plt.plot(xp[0], xp[1], 'x', color='g', ms=20)
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if i % 10 == 0:
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zs = []
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for lmark in m:
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d = sqrt((lmark[0] - xp[0, 0])**2 + (lmark[1] - xp[1, 0])**2) + randn()*sigma_r
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a = atan2(lmark[1] - xp[1, 0], lmark[0] - xp[0, 0]) - xp[2, 0] + randn()*sigma_h
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zs.append(np.array([d, a]))
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x, P = ekfloc2(x, P, u, zs, c, m, dt*10)
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if P[0,0] < 10000:
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plot_covariance_ellipse((x[0,0], x[1,0]), P[0:2, 0:2], std=2,
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facecolor='g', alpha=0.3)
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plt.plot(x[0], x[1], 'x', color='r')
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plt.axis('equal')
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plt.show()
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