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from abc import ABC, abstractmethod
import numpy as np
import rospy
from geometry_msgs.msg import Point32
from sensor_msgs.msg import ChannelFloat32, PointCloud
class ParticleCloud(ABC):
PARTICLE_COUNT = 10000
RESAMPLE_PROB = 2**-10
RAY_SAMPLES = 3
ALPHAS = [0.01, 0.00, 0.01, 0.00]
def __init__(self, world, robot, rng):
self._world = world
self._particles = np.array([[0.0] * 4] * self.PARTICLE_COUNT)
self._publisher = rospy.Publisher('/robot{}/particles'.format(robot), PointCloud, queue_size=1)
self._frame = 'robot{}/odom'.format(robot)
self._robot = robot
self._rng = rng
def resample_all(self):
self.resample(np.array([True] * self.PARTICLE_COUNT))
def resample(self, mask):
tmp = self._particles[mask,:]
self._world.sample(tmp)
self._particles[mask,:] = tmp
def move(self, delta):
rot1 = np.arctan2(delta[1], delta[0])
# Assume large rot1 means reversing
if rot1 < -np.pi:
rot1 += np.pi
elif rot1 > np.pi:
rot1 -= np.pi
trans = np.sqrt(delta[0]**2 + delta[1]**2)
rot2 = delta[2] - rot1
rot1_var = self.ALPHAS[0] * rot1**2 + self.ALPHAS[1] * trans**2
trans_var = self.ALPHAS[2] * trans**2 + self.ALPHAS[3] * (rot1**2 + rot2**2)
rot2_var = self.ALPHAS[0] * rot2**2 + self.ALPHAS[1] * trans**2
rot1 += self._rng.normal(scale = np.sqrt(rot1_var), size=self.PARTICLE_COUNT)
trans += self._rng.normal(scale = np.sqrt(trans_var), size=self.PARTICLE_COUNT)
rot2 += self._rng.normal(scale = np.sqrt(rot2_var), size=self.PARTICLE_COUNT)
self._particles[:,0] += trans * np.cos(rot1 + self.yaws)
self._particles[:,1] += trans * np.sin(rot1 + self.yaws)
self._particles[:,2] += rot1 + rot2
mask = self._rng.random(self.PARTICLE_COUNT) < self.RESAMPLE_PROB
self.resample(mask)
@abstractmethod
def calculate_weight(self, angle_dists, robot_guesses, robot_scans):
raise NotImplementedError()
def average(self):
biggest = max(self._particles[:,3])
mask = self._particles[:,3] > biggest - 5
masked = self._particles[mask]
if len(masked) < 20 or not np.isfinite(biggest):
print('Massive collapse for robot', self._robot)
self._particles[:,3] = 0.
self.resample_all()
return
weights = np.exp(masked[:,3] - max(masked[:,3]))
self._particles = self._rng.choice(masked, size=self.PARTICLE_COUNT, p=weights/np.sum(weights))
self._particles[:,3] = 0.0
def guess_position(self):
weights = np.exp(self._particles[:,3])
weights /= np.sum(weights)
mean = np.average(self._particles[:,:2], weights=weights, axis=0)
cov = np.cov(self._particles[:,:2], rowvar=False, ddof=0, aweights=weights)
# x and y being too correlated is suspicious
if abs(cov[0,0]*cov[1,1] - cov[0,1] * cov[1,0]) < 1e-15:
print('Singular covariance matrix for robot', self._robot)
# pretend to uncorrelate the data slightly
cov[0,0] += 0.1
cov[1,1] += 0.1
return mean, cov
def publish(self, frame_id):
msg = PointCloud()
msg.header.seq = frame_id
msg.header.stamp = rospy.Time.now()
msg.header.frame_id = self._frame
intensity = ChannelFloat32()
intensity.name = 'intensity'
msg.channels.append(intensity)
msg.points = [Point32(x = particle[0], y = particle[1], z = 0.05) for particle in self._particles]
intensity.values = list(np.exp(self._particles[:,3]))
self._publisher.publish(msg)
@property
def particles(self):
return self._particles
@property
def yaws(self):
return self._particles[:,2]
@property
def log_weights(self):
return self._particles[:,3]
@property
def weights(self):
return np.exp(self.log_weights)
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