基于Python实现粒子滤波效果


Posted in Python onDecember 01, 2020

1、建立仿真模型

(1)假设有一辆小车在一平面运动,起始坐标为[0,0],运动速度为1m/s,加速度为0.1 m / s 2 m/s^2 m/s2,则可以建立如下的状态方程:
Y = A ∗ X + B ∗ U Y=A*X+B*U Y=A∗X+B∗U
U为速度和加速度的的矩阵
U = [ 1 0.1 ] U= \begin{bmatrix} 1 \\ 0.1\\ \end{bmatrix} U=[10.1​]
X为当前时刻的坐标,速度,加速度
X = [ x y y a w V ] X= \begin{bmatrix} x \\ y \\ yaw \\ V \end{bmatrix} X=⎣⎢⎢⎡​xyyawV​⎦⎥⎥⎤​
Y为下一时刻的状态
则观察矩阵A为:
A = [ 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 ] A= \begin{bmatrix} 1&0 & 0 &0 \\ 0 & 1 & 0&0 \\ 0 & 0 &1 &0 \\ 0&0 & 0 &0 \end{bmatrix} A=⎣⎢⎢⎡​1000​0100​0010​0000​⎦⎥⎥⎤​
矩阵B则决定小车的运动规矩,这里取B为:
B = [ c o s ( x ) ∗ t 0 s i n ( x ) ∗ t 0 0 t 1 0 ] B= \begin{bmatrix} cos(x)*t &0\\ sin(x)*t &0\\ 0&t\\ 1&0 \end{bmatrix} B=⎣⎢⎢⎡​cos(x)∗tsin(x)∗t01​00t0​⎦⎥⎥⎤​
python编程实现小车的运动轨迹:

"""

Particle Filter localization sample

author: Atsushi Sakai (@Atsushi_twi)

"""

import math

import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial.transform import Rotation as Rot


DT = 0.1 # time tick [s]
SIM_TIME = 50.0 # simulation time [s]
MAX_RANGE = 20.0 # maximum observation range

# Particle filter parameter
NP = 100 # Number of Particle
NTh = NP / 2.0 # Number of particle for re-sampling

def calc_input():
  v = 1.0 # [m/s]
  yaw_rate = 0.1 # [rad/s]
  u = np.array([[v, yaw_rate]]).T
  return u

def motion_model(x, u):
  F = np.array([[1.0, 0, 0, 0],
         [0, 1.0, 0, 0],
         [0, 0, 1.0, 0],
         [0, 0, 0, 0]])

  B = np.array([[DT * math.cos(x[2, 0]), 0],
         [DT * math.sin(x[2, 0]), 0],
         [0.0, DT],
         [1.0, 0.0]])

  x = F.dot(x) + B.dot(u)

  return x

def main():
  print(__file__ + " start!!")

  time = 0.0
  # State Vector [x y yaw v]'
  x_true = np.zeros((4, 1))
  
  x = []
  y = []

  while SIM_TIME >= time:
    time += DT
    u = calc_input()

    x_true = motion_model(x_true, u)
    
    x.append(x_true[0])
    y.append(x_true[1])
    
  plt.plot(x,y, "-b")
    
if __name__ == '__main__':
  main()

运行结果:

基于Python实现粒子滤波效果

2、生成观测数据

实际运用中,我们需要对小车的位置进行定位,假设坐标系上有4个观测点,在小车运动过程中,需要定时将小车距离这4个观测点的位置距离记录下来,这样,当小车下一次寻迹时就有了参考点;

def observation(x_true, xd, u, rf_id):
  x_true = motion_model(x_true, u)

  # add noise to gps x-y
  z = np.zeros((0, 3))

  for i in range(len(rf_id[:, 0])):

    dx = x_true[0, 0] - rf_id[i, 0]
    dy = x_true[1, 0] - rf_id[i, 1]
    d = math.hypot(dx, dy)
    if d <= MAX_RANGE:
      dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise
      zi = np.array([[dn, rf_id[i, 0], rf_id[i, 1]]])
      z = np.vstack((z, zi))

  # add noise to input
  ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5
  ud2 = u[1, 0] + np.random.randn() * R_sim[1, 1] ** 0.5
  ud = np.array([[ud1, ud2]]).T

  xd = motion_model(xd, ud)

  return x_true, z, xd, ud

3、实现粒子滤波

#
def gauss_likelihood(x, sigma):
  p = 1.0 / math.sqrt(2.0 * math.pi * sigma ** 2) * \
    math.exp(-x ** 2 / (2 * sigma ** 2))

  return p

def pf_localization(px, pw, z, u):
  """
  Localization with Particle filter
  """

  for ip in range(NP):
    x = np.array([px[:, ip]]).T
    w = pw[0, ip]

    # 预测输入
    ud1 = u[0, 0] + np.random.randn() * R[0, 0] ** 0.5
    ud2 = u[1, 0] + np.random.randn() * R[1, 1] ** 0.5
    ud = np.array([[ud1, ud2]]).T
    x = motion_model(x, ud)

    # 计算权重
    for i in range(len(z[:, 0])):
      dx = x[0, 0] - z[i, 1]
      dy = x[1, 0] - z[i, 2]
      pre_z = math.hypot(dx, dy)
      dz = pre_z - z[i, 0]
      w = w * gauss_likelihood(dz, math.sqrt(Q[0, 0]))

    px[:, ip] = x[:, 0]
    pw[0, ip] = w

  pw = pw / pw.sum() # 归一化

  x_est = px.dot(pw.T)
  p_est = calc_covariance(x_est, px, pw)
  #计算有效粒子数
  N_eff = 1.0 / (pw.dot(pw.T))[0, 0] 
  #重采样
  if N_eff < NTh:
    px, pw = re_sampling(px, pw)
  return x_est, p_est, px, pw


def re_sampling(px, pw):
  """
  low variance re-sampling
  """

  w_cum = np.cumsum(pw)
  base = np.arange(0.0, 1.0, 1 / NP)
  re_sample_id = base + np.random.uniform(0, 1 / NP)
  indexes = []
  ind = 0
  for ip in range(NP):
    while re_sample_id[ip] > w_cum[ind]:
      ind += 1
    indexes.append(ind)

  px = px[:, indexes]
  pw = np.zeros((1, NP)) + 1.0 / NP # init weight

  return px, pw

4、完整源码

该代码来源于https://github.com/AtsushiSakai/PythonRobotics

"""

Particle Filter localization sample

author: Atsushi Sakai (@Atsushi_twi)

"""

import math

import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial.transform import Rotation as Rot

# Estimation parameter of PF
Q = np.diag([0.2]) ** 2 # range error
R = np.diag([2.0, np.deg2rad(40.0)]) ** 2 # input error

# Simulation parameter
Q_sim = np.diag([0.2]) ** 2
R_sim = np.diag([1.0, np.deg2rad(30.0)]) ** 2

DT = 0.1 # time tick [s]
SIM_TIME = 50.0 # simulation time [s]
MAX_RANGE = 20.0 # maximum observation range

# Particle filter parameter
NP = 100 # Number of Particle
NTh = NP / 2.0 # Number of particle for re-sampling

show_animation = True


def calc_input():
  v = 1.0 # [m/s]
  yaw_rate = 0.1 # [rad/s]
  u = np.array([[v, yaw_rate]]).T
  return u


def observation(x_true, xd, u, rf_id):
  x_true = motion_model(x_true, u)

  # add noise to gps x-y
  z = np.zeros((0, 3))

  for i in range(len(rf_id[:, 0])):

    dx = x_true[0, 0] - rf_id[i, 0]
    dy = x_true[1, 0] - rf_id[i, 1]
    d = math.hypot(dx, dy)
    if d <= MAX_RANGE:
      dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise
      zi = np.array([[dn, rf_id[i, 0], rf_id[i, 1]]])
      z = np.vstack((z, zi))

  # add noise to input
  ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5
  ud2 = u[1, 0] + np.random.randn() * R_sim[1, 1] ** 0.5
  ud = np.array([[ud1, ud2]]).T

  xd = motion_model(xd, ud)

  return x_true, z, xd, ud


def motion_model(x, u):
  F = np.array([[1.0, 0, 0, 0],
         [0, 1.0, 0, 0],
         [0, 0, 1.0, 0],
         [0, 0, 0, 0]])

  B = np.array([[DT * math.cos(x[2, 0]), 0],
         [DT * math.sin(x[2, 0]), 0],
         [0.0, DT],
         [1.0, 0.0]])

  x = F.dot(x) + B.dot(u)

  return x


def gauss_likelihood(x, sigma):
  p = 1.0 / math.sqrt(2.0 * math.pi * sigma ** 2) * \
    math.exp(-x ** 2 / (2 * sigma ** 2))

  return p


def calc_covariance(x_est, px, pw):
  """
  calculate covariance matrix
  see ipynb doc
  """
  cov = np.zeros((3, 3))
  n_particle = px.shape[1]
  for i in range(n_particle):
    dx = (px[:, i:i + 1] - x_est)[0:3]
    cov += pw[0, i] * dx @ dx.T
  cov *= 1.0 / (1.0 - pw @ pw.T)

  return cov


def pf_localization(px, pw, z, u):
  """
  Localization with Particle filter
  """

  for ip in range(NP):
    x = np.array([px[:, ip]]).T
    w = pw[0, ip]

    # Predict with random input sampling
    ud1 = u[0, 0] + np.random.randn() * R[0, 0] ** 0.5
    ud2 = u[1, 0] + np.random.randn() * R[1, 1] ** 0.5
    ud = np.array([[ud1, ud2]]).T
    x = motion_model(x, ud)

    # Calc Importance Weight
    for i in range(len(z[:, 0])):
      dx = x[0, 0] - z[i, 1]
      dy = x[1, 0] - z[i, 2]
      pre_z = math.hypot(dx, dy)
      dz = pre_z - z[i, 0]
      w = w * gauss_likelihood(dz, math.sqrt(Q[0, 0]))

    px[:, ip] = x[:, 0]
    pw[0, ip] = w

  pw = pw / pw.sum() # normalize

  x_est = px.dot(pw.T)
  p_est = calc_covariance(x_est, px, pw)

  N_eff = 1.0 / (pw.dot(pw.T))[0, 0] # Effective particle number
  if N_eff < NTh:
    px, pw = re_sampling(px, pw)
  return x_est, p_est, px, pw


def re_sampling(px, pw):
  """
  low variance re-sampling
  """

  w_cum = np.cumsum(pw)
  base = np.arange(0.0, 1.0, 1 / NP)
  re_sample_id = base + np.random.uniform(0, 1 / NP)
  indexes = []
  ind = 0
  for ip in range(NP):
    while re_sample_id[ip] > w_cum[ind]:
      ind += 1
    indexes.append(ind)

  px = px[:, indexes]
  pw = np.zeros((1, NP)) + 1.0 / NP # init weight

  return px, pw


def plot_covariance_ellipse(x_est, p_est): # pragma: no cover
  p_xy = p_est[0:2, 0:2]
  eig_val, eig_vec = np.linalg.eig(p_xy)

  if eig_val[0] >= eig_val[1]:
    big_ind = 0
    small_ind = 1
  else:
    big_ind = 1
    small_ind = 0

  t = np.arange(0, 2 * math.pi + 0.1, 0.1)

  # eig_val[big_ind] or eiq_val[small_ind] were occasionally negative
  # numbers extremely close to 0 (~10^-20), catch these cases and set the
  # respective variable to 0
  try:
    a = math.sqrt(eig_val[big_ind])
  except ValueError:
    a = 0

  try:
    b = math.sqrt(eig_val[small_ind])
  except ValueError:
    b = 0

  x = [a * math.cos(it) for it in t]
  y = [b * math.sin(it) for it in t]
  angle = math.atan2(eig_vec[1, big_ind], eig_vec[0, big_ind])
  rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
  fx = rot.dot(np.array([[x, y]]))
  px = np.array(fx[0, :] + x_est[0, 0]).flatten()
  py = np.array(fx[1, :] + x_est[1, 0]).flatten()
  plt.plot(px, py, "--r")


def main():
  print(__file__ + " start!!")

  time = 0.0

  # RF_ID positions [x, y]
  rf_id = np.array([[10.0, 0.0],
           [10.0, 10.0],
           [0.0, 15.0],
           [-5.0, 20.0]])

  # State Vector [x y yaw v]'
  x_est = np.zeros((4, 1))
  x_true = np.zeros((4, 1))

  px = np.zeros((4, NP)) # Particle store
  pw = np.zeros((1, NP)) + 1.0 / NP # Particle weight
  x_dr = np.zeros((4, 1)) # Dead reckoning

  # history
  h_x_est = x_est
  h_x_true = x_true
  h_x_dr = x_true

  while SIM_TIME >= time:
    time += DT
    u = calc_input()

    x_true, z, x_dr, ud = observation(x_true, x_dr, u, rf_id)

    x_est, PEst, px, pw = pf_localization(px, pw, z, ud)

    # store data history
    h_x_est = np.hstack((h_x_est, x_est))
    h_x_dr = np.hstack((h_x_dr, x_dr))
    h_x_true = np.hstack((h_x_true, x_true))

    if show_animation:
      plt.cla()
      # for stopping simulation with the esc key.
      plt.gcf().canvas.mpl_connect(
        'key_release_event',
        lambda event: [exit(0) if event.key == 'escape' else None])

      for i in range(len(z[:, 0])):
        plt.plot([x_true[0, 0], z[i, 1]], [x_true[1, 0], z[i, 2]], "-k")
      plt.plot(rf_id[:, 0], rf_id[:, 1], "*k")
      plt.plot(px[0, :], px[1, :], ".r")
      plt.plot(np.array(h_x_true[0, :]).flatten(),
           np.array(h_x_true[1, :]).flatten(), "-b")
      plt.plot(np.array(h_x_dr[0, :]).flatten(),
           np.array(h_x_dr[1, :]).flatten(), "-k")
      plt.plot(np.array(h_x_est[0, :]).flatten(),
           np.array(h_x_est[1, :]).flatten(), "-r")
      plot_covariance_ellipse(x_est, PEst)
      plt.axis("equal")
      plt.grid(True)
      plt.pause(0.001)


if __name__ == '__main__':
  main()

到此这篇关于基于Python实现粒子滤波的文章就介绍到这了,更多相关Python实现粒子滤波内容请搜索三水点靠木以前的文章或继续浏览下面的相关文章希望大家以后多多支持三水点靠木!

Python 相关文章推荐
简单了解Python下用于监视文件系统的pyinotify包
Nov 13 Python
windows上安装Anaconda和python的教程详解
Mar 28 Python
使用Python设计一个代码统计工具
Apr 04 Python
浅谈python日志的配置文件路径问题
Apr 28 Python
python基于http下载视频或音频
Jun 20 Python
Python 面试中 8 个必考问题
Nov 16 Python
python 文本单词提取和词频统计的实例
Dec 22 Python
django的settings中设置中文支持的实现
Apr 28 Python
Django框架model模型对象验证实现方法分析
Oct 02 Python
Pytorch在dataloader类中设置shuffle的随机数种子方式
Jan 14 Python
基于Python爬取51cto博客页面信息过程解析
Aug 25 Python
Python创建自己的加密货币的示例
Mar 01 Python
Django集成MongoDB实现过程解析
Dec 01 #Python
基于Django快速集成Echarts代码示例
Dec 01 #Python
Python更改pip镜像源的方法示例
Dec 01 #Python
Python读取图像并显示灰度图的实现
Dec 01 #Python
Python性能测试工具Locust安装及使用
Dec 01 #Python
python爬虫中抓取指数的实例讲解
Dec 01 #Python
OpenCV灰度化之后图片为绿色的解决
Dec 01 #Python
You might like
40年前的这部特摄片恐龙特级克塞号80后的共同回忆
2020/03/08 日漫
教你如何开启shopnc b2b2c 伪静态
2014/10/21 PHP
PHP之sprintf函数用法详解
2014/11/12 PHP
PHP实现自动发送邮件功能代码(qq 邮箱)
2017/08/18 PHP
ExtJS扩展 垂直tabLayout实现代码
2009/06/21 Javascript
Mootools 1.2教程(21)——类(二)
2009/09/15 Javascript
javascript动画之圆形运动,环绕鼠标运动作小球
2010/07/20 Javascript
基于jquery的固定表头和列头的代码
2012/05/03 Javascript
JavaScript之Getters和Setters 平台支持等详细介绍
2012/12/07 Javascript
基于Jquery和html5的7款个性化地图插件
2015/11/17 Javascript
全面解析Bootstrap手风琴效果
2020/04/17 Javascript
基于javascript实现页面加载loading效果
2020/09/15 Javascript
手机浏览器 后退按钮强制刷新页面方法总结
2016/10/09 Javascript
Vue的轮播图组件实现方法
2018/03/03 Javascript
vue使用xe-utils函数库的具体方法
2018/03/06 Javascript
微信小程序实现签到功能
2018/10/31 Javascript
JavaScript实现五子棋小游戏
2020/10/26 Javascript
微信小程序中target和currentTarget的区别小结
2020/11/06 Javascript
Vue +WebSocket + WaveSurferJS 实现H5聊天对话交互的实例
2020/11/18 Vue.js
[02:49]DAC2018决赛日TOP5 LGD开启黑暗之门绝杀VP
2018/04/08 DOTA
Python使用微信SDK实现的微信支付功能示例
2017/06/30 Python
分析python切片原理和方法
2017/12/19 Python
Python 获取ftp服务器文件时间的方法
2019/07/02 Python
Python3 itchat实现微信定时发送群消息的实例代码
2019/07/12 Python
在TensorFlow中屏蔽warning的方式
2020/02/04 Python
python关于倒排列的知识点总结
2020/10/13 Python
CSS3实现超慢速移动动画效果非常流畅无卡顿
2014/06/15 HTML / CSS
Holiday Inn中国官网:IHG旗下假日酒店预订
2018/04/08 全球购物
请用Java实现列出某个目录下的所有文件
2013/09/23 面试题
副总经理岗位职责
2014/03/16 职场文书
本科毕业自我鉴定
2014/03/20 职场文书
依法行政工作汇报材料
2014/10/28 职场文书
活动费用申请报告
2015/05/15 职场文书
大国崛起观后感
2015/06/02 职场文书
指导教师推荐意见
2015/06/05 职场文书
纯CSS如何禁止用户复制网页的内容
2021/11/01 HTML / CSS