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FastSLAM1.0とFastSLAM2.0によるSLAMのためのPythonサンプルプログラム

https://github.com/AtsushiSakai/PythonRobotics/raw/master/SLAM/FastSLAM2/animation.gif

目次

はじめに

先日、拡張カルマンフィルタ(EKF)による

SLAMのサンプルコードを公開しましたが、

d.hatena.ne.jp

同じくベイズフィルタの一種であるParticle Filterを使った

FastSLAMというSLAMのアルゴリズムがあります。

myenigma.hatenablog.com

 

今回はFastSLAMを使用したSLAMの

簡単なPythonサンプルプログラムを紹介したいと思います。

 

FastSLAMアルゴリズムの概要

FastSLAMアルゴリズムは、

Particle FilterをベースとしたSLAMのアルゴリズムです。

各パーティクルがロボットの位置と姿勢を表しており、

それぞれのパーティクルがSLAM用の個別の地図を持っているのが特徴です。

 

今回のサンプルコードで紹介する特徴点ベースの地図を使った

FastSLAMの場合、

各地図のランドマークは、個別の小さいEKFを使って推定されます。

またそのランドマークの観測状況に応じて、

各パーティクルの尤度を計算します。

リサンプリングなどに関しては、

通常のパーティクルフィルタと同じアルゴリズムを利用可能です。

 

上記のアルゴリズムはFastSLAM1.0と呼ばれ、

このアルゴリズムを拡張したのがFastSLAM2.0です。

 

FastSLAM2.0は、

FastSLAM1.0の計算が多くなる原因である

パーティクルの数を減らすために、

各パーティクルの位置を観測値とEKFを使って補正することで、

少ないパーティクルで高精度なSLAMを実現するアルゴリズムです。

  

FastSLAMのアルゴリズムの詳細に関しては、

下記の書籍を参照ください。

FastSLAM1.0によるSLAMのためのPythonサンプルプログラム

"""
FastSLAM 1.0 example
author: Atsushi Sakai (@Atsushi_twi)
"""

import numpy as np
import math
import matplotlib.pyplot as plt


# Fast SLAM covariance
Q = np.diag([3.0, math.radians(10.0)])**2
R = np.diag([1.0, math.radians(20.0)])**2

#  Simulation parameter
Qsim = np.diag([0.3, math.radians(2.0)])**2
Rsim = np.diag([0.5, math.radians(10.0)])**2
OFFSET_YAWRATE_NOISE = 0.01

DT = 0.1  # time tick [s]
SIM_TIME = 50.0  # simulation time [s]
MAX_RANGE = 20.0  # maximum observation range
M_DIST_TH = 2.0  # Threshold of Mahalanobis distance for data association.
STATE_SIZE = 3  # State size [x,y,yaw]
LM_SIZE = 2  # LM srate size [x,y]
N_PARTICLE = 100  # number of particle
NTH = N_PARTICLE / 1.5  # Number of particle for re-sampling

show_animation = True


class Particle:

    def __init__(self, N_LM):
        self.w = 1.0 / N_PARTICLE
        self.x = 0.0
        self.y = 0.0
        self.yaw = 0.0
        # landmark x-y positions
        self.lm = np.matrix(np.zeros((N_LM, LM_SIZE)))
        # landmark position covariance
        self.lmP = np.matrix(np.zeros((N_LM * LM_SIZE, LM_SIZE)))


def fast_slam1(particles, u, z):

    particles = predict_particles(particles, u)

    particles = update_with_observation(particles, z)

    particles = resampling(particles)

    return particles


def normalize_weight(particles):

    sumw = sum([p.w for p in particles])

    try:
        for i in range(N_PARTICLE):
            particles[i].w /= sumw
    except ZeroDivisionError:
        for i in range(N_PARTICLE):
            particles[i].w = 1.0 / N_PARTICLE

        return particles

    return particles


def calc_final_state(particles):

    xEst = np.zeros((STATE_SIZE, 1))

    particles = normalize_weight(particles)

    for i in range(N_PARTICLE):
        xEst[0, 0] += particles[i].w * particles[i].x
        xEst[1, 0] += particles[i].w * particles[i].y
        xEst[2, 0] += particles[i].w * particles[i].yaw

    xEst[2, 0] = pi_2_pi(xEst[2, 0])
    #  print(xEst)

    return xEst


def predict_particles(particles, u):

    for i in range(N_PARTICLE):
        px = np.zeros((STATE_SIZE, 1))
        px[0, 0] = particles[i].x
        px[1, 0] = particles[i].y
        px[2, 0] = particles[i].yaw
        ud = u + (np.matrix(np.random.randn(1, 2)) * R).T  # add noise
        px = motion_model(px, ud)
        particles[i].x = px[0, 0]
        particles[i].y = px[1, 0]
        particles[i].yaw = px[2, 0]

    return particles


def add_new_lm(particle, z, Q):

    r = z[0, 0]
    b = z[0, 1]
    lm_id = int(z[0, 2])

    s = math.sin(pi_2_pi(particle.yaw + b))
    c = math.cos(pi_2_pi(particle.yaw + b))

    particle.lm[lm_id, 0] = particle.x + r * c
    particle.lm[lm_id, 1] = particle.y + r * s

    # covariance
    Gz = np.matrix([[c, -r * s],
                    [s, r * c]])

    particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz * Q * Gz.T

    return particle


def compute_jacobians(particle, xf, Pf, Q):
    dx = xf[0, 0] - particle.x
    dy = xf[1, 0] - particle.y
    d2 = dx**2 + dy**2
    d = math.sqrt(d2)

    zp = np.matrix([[d, pi_2_pi(math.atan2(dy, dx) - particle.yaw)]]).T

    Hv = np.matrix([[-dx / d, -dy / d, 0.0],
                    [dy / d2, -dx / d2, -1.0]])

    Hf = np.matrix([[dx / d, dy / d],
                    [-dy / d2, dx / d2]])

    Sf = Hf * Pf * Hf.T + Q

    return zp, Hv, Hf, Sf


def update_KF_with_cholesky(xf, Pf, v, Q, Hf):
    PHt = Pf * Hf.T
    S = Hf * PHt + Q

    S = (S + S.T) * 0.5
    SChol = np.linalg.cholesky(S).T
    SCholInv = np.linalg.inv(SChol)
    W1 = PHt * SCholInv
    W = W1 * SCholInv.T

    x = xf + W * v
    P = Pf - W1 * W1.T

    return x, P


def update_landmark(particle, z, Q):

    lm_id = int(z[0, 2])
    xf = np.matrix(particle.lm[lm_id, :]).T
    Pf = np.matrix(particle.lmP[2 * lm_id:2 * lm_id + 2, :])

    zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q)

    dz = z[0, 0: 2].T - zp
    dz[1, 0] = pi_2_pi(dz[1, 0])

    xf, Pf = update_KF_with_cholesky(xf, Pf, dz, Q, Hf)

    particle.lm[lm_id, :] = xf.T
    particle.lmP[2 * lm_id:2 * lm_id + 2, :] = Pf

    return particle


def compute_weight(particle, z, Q):

    lm_id = int(z[0, 2])
    xf = np.matrix(particle.lm[lm_id, :]).T
    Pf = np.matrix(particle.lmP[2 * lm_id:2 * lm_id + 2])
    zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q)

    dx = z[0, 0: 2].T - zp
    dx[1, 0] = pi_2_pi(dx[1, 0])

    S = particle.lmP[2 * lm_id:2 * lm_id + 2]
    try:
        invS = np.linalg.inv(S)
    except np.linalg.linalg.LinAlgError:
        print("singuler")
        return 1.0

    num = math.exp(-0.5 * dx.T * invS * dx)
    den = 2.0 * math.pi * math.sqrt(np.linalg.det(S))

    w = num / den

    return w


def update_with_observation(particles, z):

    for iz in range(len(z[:, 0])):

        lmid = int(z[iz, 2])

        for ip in range(N_PARTICLE):
            # new landmark
            if abs(particles[ip].lm[lmid, 0]) <= 0.01:
                particles[ip] = add_new_lm(particles[ip], z[iz, :], Q)
            # known landmark
            else:
                w = compute_weight(particles[ip], z[iz, :], Q)
                particles[ip].w *= w
                particles[ip] = update_landmark(particles[ip], z[iz, :], Q)

    return particles


def resampling(particles):
    """
    low variance re-sampling
    """

    particles = normalize_weight(particles)

    pw = []
    for i in range(N_PARTICLE):
        pw.append(particles[i].w)

    pw = np.matrix(pw)

    Neff = 1.0 / (pw * pw.T)[0, 0]  # Effective particle number
    #  print(Neff)

    if Neff < NTH:  # resampling
        wcum = np.cumsum(pw)
        base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE
        resampleid = base + np.random.rand(base.shape[1]) / N_PARTICLE

        inds = []
        ind = 0
        for ip in range(N_PARTICLE):
            while ((ind < wcum.shape[1] - 1) and (resampleid[0, ip] > wcum[0, ind])):
                ind += 1
            inds.append(ind)

        tparticles = particles[:]
        for i in range(len(inds)):
            particles[i].x = tparticles[inds[i]].x
            particles[i].y = tparticles[inds[i]].y
            particles[i].yaw = tparticles[inds[i]].yaw
            particles[i].lm = tparticles[inds[i]].lm[:, :]
            particles[i].lmP = tparticles[inds[i]].lmP[:, :]
            particles[i].w = 1.0 / N_PARTICLE

    return particles


def calc_input(time):

    if time <= 3.0:
        v = 0.0
        yawrate = 0.0
    else:
        v = 1.0  # [m/s]
        yawrate = 0.1  # [rad/s]

    u = np.matrix([v, yawrate]).T

    return u


def observation(xTrue, xd, u, RFID):

    xTrue = motion_model(xTrue, u)

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

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

        dx = RFID[i, 0] - xTrue[0, 0]
        dy = RFID[i, 1] - xTrue[1, 0]
        d = math.sqrt(dx**2 + dy**2)
        angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
        if d <= MAX_RANGE:
            dn = d + np.random.randn() * Qsim[0, 0]  # add noise
            anglen = angle + np.random.randn() * Qsim[1, 1]  # add noise
            zi = np.matrix([dn, pi_2_pi(anglen), i])
            z = np.vstack((z, zi))

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

    xd = motion_model(xd, ud)

    return xTrue, z, xd, ud


def motion_model(x, u):

    F = np.matrix([[1.0, 0, 0],
                   [0, 1.0, 0],
                   [0, 0, 1.0]])

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

    x = F * x + B * u

    x[2, 0] = pi_2_pi(x[2, 0])

    return x


def pi_2_pi(angle):
    return (angle + math.pi) % (2*math.pi) - math.pi


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

    time = 0.0

    # RFID positions [x, y]
    RFID = np.array([[10.0, -2.0],
                     [15.0, 10.0],
                     [15.0, 15.0],
                     [10.0, 20.0],
                     [3.0, 15.0],
                     [-5.0, 20.0],
                     [-5.0, 5.0],
                     [-10.0, 15.0]
                     ])
    N_LM = RFID.shape[0]

    # State Vector [x y yaw v]'
    xEst = np.matrix(np.zeros((STATE_SIZE, 1)))
    xTrue = np.matrix(np.zeros((STATE_SIZE, 1)))

    xDR = np.matrix(np.zeros((STATE_SIZE, 1)))  # Dead reckoning

    # history
    hxEst = xEst
    hxTrue = xTrue
    hxDR = xTrue

    particles = [Particle(N_LM) for i in range(N_PARTICLE)]

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

        xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)

        particles = fast_slam1(particles, ud, z)

        xEst = calc_final_state(particles)

        x_state = xEst[0: STATE_SIZE]

        # store data history
        hxEst = np.hstack((hxEst, x_state))
        hxDR = np.hstack((hxDR, xDR))
        hxTrue = np.hstack((hxTrue, xTrue))

        if show_animation:
            plt.cla()
            plt.plot(RFID[:, 0], RFID[:, 1], "*k")

            for i in range(N_PARTICLE):
                plt.plot(particles[i].x, particles[i].y, ".r")
                plt.plot(particles[i].lm[:, 0], particles[i].lm[:, 1], "xb")

            plt.plot(np.array(hxTrue[0, :]).flatten(),
                     np.array(hxTrue[1, :]).flatten(), "-b")
            plt.plot(np.array(hxDR[0, :]).flatten(),
                     np.array(hxDR[1, :]).flatten(), "-k")
            plt.plot(np.array(hxEst[0, :]).flatten(),
                     np.array(hxEst[1, :]).flatten(), "-r")

            plt.plot(xEst[0], xEst[1], "xk")
            plt.axis("equal")
            plt.grid(True)
            plt.pause(0.001)


if __name__ == '__main__':
    main()

下記のGitHubリポジトリでもコードを公開しています。

github.com

このコードを実行すると、

記事冒頭のようなSLAMのシミュレーションが実行されるはずです。

青線は真の軌跡、

黒線はDead Reckoningの軌跡、

黒丸は真のランドマークの位置、

緑バツは推定されたランドマークの位置、

赤線はFast SLAMで推定された軌跡です。

 

FastSLAM2.0によるSLAMのためのPythonサンプルプログラム

"""
FastSLAM 2.0 example
author: Atsushi Sakai (@Atsushi_twi)
"""

import numpy as np
import math
import matplotlib.pyplot as plt


# Fast SLAM covariance
Q = np.diag([3.0, math.radians(10.0)])**2
R = np.diag([1.0, math.radians(20.0)])**2

#  Simulation parameter
Qsim = np.diag([0.3, math.radians(2.0)])**2
Rsim = np.diag([0.5, math.radians(10.0)])**2
OFFSET_YAWRATE_NOISE = 0.01

DT = 0.1  # time tick [s]
SIM_TIME = 50.0  # simulation time [s]
MAX_RANGE = 20.0  # maximum observation range
M_DIST_TH = 2.0  # Threshold of Mahalanobis distance for data association.
STATE_SIZE = 3  # State size [x,y,yaw]
LM_SIZE = 2  # LM srate size [x,y]
N_PARTICLE = 100  # number of particle
NTH = N_PARTICLE / 1.0  # Number of particle for re-sampling

show_animation = True


class Particle:

    def __init__(self, N_LM):
        self.w = 1.0 / N_PARTICLE
        self.x = 0.0
        self.y = 0.0
        self.yaw = 0.0
        self.P = np.eye(3)
        # landmark x-y positions
        self.lm = np.matrix(np.zeros((N_LM, LM_SIZE)))
        # landmark position covariance
        self.lmP = np.matrix(np.zeros((N_LM * LM_SIZE, LM_SIZE)))


def fast_slam2(particles, u, z):

    particles = predict_particles(particles, u)

    particles = update_with_observation(particles, z)

    particles = resampling(particles)

    return particles


def normalize_weight(particles):

    sumw = sum([p.w for p in particles])

    try:
        for i in range(N_PARTICLE):
            particles[i].w /= sumw
    except ZeroDivisionError:
        for i in range(N_PARTICLE):
            particles[i].w = 1.0 / N_PARTICLE

        return particles

    return particles


def calc_final_state(particles):

    xEst = np.zeros((STATE_SIZE, 1))

    particles = normalize_weight(particles)

    for i in range(N_PARTICLE):
        xEst[0, 0] += particles[i].w * particles[i].x
        xEst[1, 0] += particles[i].w * particles[i].y
        xEst[2, 0] += particles[i].w * particles[i].yaw

    xEst[2, 0] = pi_2_pi(xEst[2, 0])
    #  print(xEst)

    return xEst


def predict_particles(particles, u):

    for i in range(N_PARTICLE):
        px = np.zeros((STATE_SIZE, 1))
        px[0, 0] = particles[i].x
        px[1, 0] = particles[i].y
        px[2, 0] = particles[i].yaw
        ud = u + (np.matrix(np.random.randn(1, 2)) * R).T  # add noise
        px = motion_model(px, ud)
        particles[i].x = px[0, 0]
        particles[i].y = px[1, 0]
        particles[i].yaw = px[2, 0]

    return particles


def add_new_lm(particle, z, Q):

    r = z[0, 0]
    b = z[0, 1]
    lm_id = int(z[0, 2])

    s = math.sin(pi_2_pi(particle.yaw + b))
    c = math.cos(pi_2_pi(particle.yaw + b))

    particle.lm[lm_id, 0] = particle.x + r * c
    particle.lm[lm_id, 1] = particle.y + r * s

    # covariance
    Gz = np.matrix([[c, -r * s],
                    [s, r * c]])

    particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz * Q * Gz.T

    return particle


def compute_jacobians(particle, xf, Pf, Q):
    dx = xf[0, 0] - particle.x
    dy = xf[1, 0] - particle.y
    d2 = dx**2 + dy**2
    d = math.sqrt(d2)

    zp = np.matrix([[d, pi_2_pi(math.atan2(dy, dx) - particle.yaw)]]).T

    Hv = np.matrix([[-dx / d, -dy / d, 0.0],
                    [dy / d2, -dx / d2, -1.0]])

    Hf = np.matrix([[dx / d, dy / d],
                    [-dy / d2, dx / d2]])

    Sf = Hf * Pf * Hf.T + Q

    return zp, Hv, Hf, Sf


def update_KF_with_cholesky(xf, Pf, v, Q, Hf):
    PHt = Pf * Hf.T
    S = Hf * PHt + Q

    S = (S + S.T) * 0.5
    SChol = np.linalg.cholesky(S).T
    SCholInv = np.linalg.inv(SChol)
    W1 = PHt * SCholInv
    W = W1 * SCholInv.T

    x = xf + W * v
    P = Pf - W1 * W1.T

    return x, P


def update_landmark(particle, z, Q):

    lm_id = int(z[0, 2])
    xf = np.matrix(particle.lm[lm_id, :]).T
    Pf = np.matrix(particle.lmP[2 * lm_id:2 * lm_id + 2, :])

    zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q)

    dz = z[0, 0: 2].T - zp
    dz[1, 0] = pi_2_pi(dz[1, 0])

    xf, Pf = update_KF_with_cholesky(xf, Pf, dz, Q, Hf)

    particle.lm[lm_id, :] = xf.T
    particle.lmP[2 * lm_id:2 * lm_id + 2, :] = Pf

    return particle


def compute_weight(particle, z, Q):

    lm_id = int(z[0, 2])
    xf = np.matrix(particle.lm[lm_id, :]).T
    Pf = np.matrix(particle.lmP[2 * lm_id:2 * lm_id + 2])
    zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q)

    dz = z[0, 0: 2].T - zp
    dz[1, 0] = pi_2_pi(dz[1, 0])

    S = particle.lmP[2 * lm_id:2 * lm_id + 2]
    try:
        invS = np.linalg.inv(S)
    except np.linalg.linalg.LinAlgError:
        print("singuler")
        return 1.0

    num = math.exp(-0.5 * dz.T * invS * dz)
    den = 2.0 * math.pi * math.sqrt(np.linalg.det(S))

    w = num / den

    return w


def proposal_sampling(particle, z, Q):

    lm_id = int(z[0, 2])
    xf = np.matrix(particle.lm[lm_id, :]).T
    Pf = np.matrix(particle.lmP[2 * lm_id:2 * lm_id + 2])
    # State
    x = np.matrix([[particle.x, particle.y, particle.yaw]]).T
    P = particle.P
    zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q)

    Sfi = np.linalg.inv(Sf)
    dz = z[0, 0: 2].T - zp
    dz[1, 0] = pi_2_pi(dz[1, 0])

    Pi = np.linalg.inv(P)

    particle.P = np.linalg.inv(Hv.T * Sfi * Hv + Pi)  # proposal covariance
    x += particle.P * Hv.T * Sfi * dz  # proposal mean

    particle.x = x[0, 0]
    particle.y = x[1, 0]
    particle.yaw = x[2, 0]

    return particle


def update_with_observation(particles, z):

    for iz in range(len(z[:, 0])):

        lmid = int(z[iz, 2])

        for ip in range(N_PARTICLE):
            # new landmark
            if abs(particles[ip].lm[lmid, 0]) <= 0.01:
                particles[ip] = add_new_lm(particles[ip], z[iz, :], Q)
            # known landmark
            else:
                w = compute_weight(particles[ip], z[iz, :], Q)
                particles[ip].w *= w

                particles[ip] = update_landmark(particles[ip], z[iz, :], Q)
                particles[ip] = proposal_sampling(particles[ip], z[iz, :], Q)

    return particles


def resampling(particles):
    """
    low variance re-sampling
    """

    particles = normalize_weight(particles)

    pw = []
    for i in range(N_PARTICLE):
        pw.append(particles[i].w)

    pw = np.matrix(pw)

    Neff = 1.0 / (pw * pw.T)[0, 0]  # Effective particle number
    #  print(Neff)

    if Neff < NTH:  # resampling
        wcum = np.cumsum(pw)
        base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE
        resampleid = base + np.random.rand(base.shape[1]) / N_PARTICLE

        inds = []
        ind = 0
        for ip in range(N_PARTICLE):
            while ((ind < wcum.shape[1] - 1) and (resampleid[0, ip] > wcum[0, ind])):
                ind += 1
            inds.append(ind)

        tparticles = particles[:]
        for i in range(len(inds)):
            particles[i].x = tparticles[inds[i]].x
            particles[i].y = tparticles[inds[i]].y
            particles[i].yaw = tparticles[inds[i]].yaw
            particles[i].lm = tparticles[inds[i]].lm[:, :]
            particles[i].lmP = tparticles[inds[i]].lmP[:, :]
            particles[i].w = 1.0 / N_PARTICLE

    return particles


def calc_input(time):

    if time <= 3.0:
        v = 0.0
        yawrate = 0.0
    else:
        v = 1.0  # [m/s]
        yawrate = 0.1  # [rad/s]

    u = np.matrix([v, yawrate]).T

    return u


def observation(xTrue, xd, u, RFID):

    xTrue = motion_model(xTrue, u)

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

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

        dx = RFID[i, 0] - xTrue[0, 0]
        dy = RFID[i, 1] - xTrue[1, 0]
        d = math.sqrt(dx**2 + dy**2)
        angle = math.atan2(dy, dx) - xTrue[2, 0]
        if d <= MAX_RANGE:
            dn = d + np.random.randn() * Qsim[0, 0]  # add noise
            anglen = angle + np.random.randn() * Qsim[1, 1]  # add noise
            zi = np.matrix([dn, pi_2_pi(anglen), i])
            z = np.vstack((z, zi))

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

    xd = motion_model(xd, ud)

    return xTrue, z, xd, ud


def motion_model(x, u):

    F = np.matrix([[1.0, 0, 0],
                   [0, 1.0, 0],
                   [0, 0, 1.0]])

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

    x = F * x + B * u

    x[2, 0] = pi_2_pi(x[2, 0])

    return x


def pi_2_pi(angle):
    return (angle + math.pi) % (2*math.pi) - math.pi


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

    time = 0.0

    # RFID positions [x, y]
    RFID = np.array([[10.0, -2.0],
                     [15.0, 10.0],
                     [15.0, 15.0],
                     [10.0, 20.0],
                     [3.0, 15.0],
                     [-5.0, 20.0],
                     [-5.0, 5.0],
                     [-10.0, 15.0]
                     ])
    N_LM = RFID.shape[0]

    # State Vector [x y yaw v]'
    xEst = np.matrix(np.zeros((STATE_SIZE, 1)))
    xTrue = np.matrix(np.zeros((STATE_SIZE, 1)))

    xDR = np.matrix(np.zeros((STATE_SIZE, 1)))  # Dead reckoning

    # history
    hxEst = xEst
    hxTrue = xTrue
    hxDR = xTrue

    particles = [Particle(N_LM) for i in range(N_PARTICLE)]

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

        xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)

        particles = fast_slam2(particles, ud, z)

        xEst = calc_final_state(particles)

        x_state = xEst[0: STATE_SIZE]

        # store data history
        hxEst = np.hstack((hxEst, x_state))
        hxDR = np.hstack((hxDR, xDR))
        hxTrue = np.hstack((hxTrue, xTrue))

        if show_animation:
            plt.cla()
            plt.plot(RFID[:, 0], RFID[:, 1], "*k")

            for iz in range(len(z[:, 0])):
                lmid = int(z[iz, 2])
                plt.plot([xEst[0, 0], RFID[lmid, 0]], [
                         xEst[1, 0], RFID[lmid, 1]], "-k")

            for i in range(N_PARTICLE):
                plt.plot(particles[i].x, particles[i].y, ".r")
                plt.plot(particles[i].lm[:, 0], particles[i].lm[:, 1], "xb")

            plt.plot(np.array(hxTrue[0, :]).flatten(),
                     np.array(hxTrue[1, :]).flatten(), "-b")
            plt.plot(np.array(hxDR[0, :]).flatten(),
                     np.array(hxDR[1, :]).flatten(), "-k")
            plt.plot(np.array(hxEst[0, :]).flatten(),
                     np.array(hxEst[1, :]).flatten(), "-r")

            plt.plot(xEst[0], xEst[1], "xk")
            plt.axis("equal")
            plt.grid(True)
            plt.pause(0.001)


if __name__ == '__main__':
    main()

下記のGitHubリポジトリでもコードを公開しています。

github.com

このコードを実行すると、

記事冒頭のようなSLAMのシミュレーションが実行されるはずです。

参考資料

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