notebook.community

Edit and run 





In [1]:



    


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import copy
import montecarlo_localization as mcl
%load_ext autoreload
%autoreload 2
#%matplotlib inline
plt.style.use('ggplot')

Development Notes

Method for extracting particles by weight





In [154]:



    


a = np.array([p for p in range(10)])
a



    


















    
Out[154]:








array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

















In [155]:



    


b = a / a.sum()
b



    


















    
Out[155]:








array([ 0.        ,  0.02222222,  0.04444444,  0.06666667,  0.08888889,
        0.11111111,  0.13333333,  0.15555556,  0.17777778,  0.2       ])

















In [156]:



    


choices = np.random.multinomial(len(a), b)
choices



    


















    
Out[156]:








array([0, 0, 0, 0, 1, 2, 0, 0, 4, 3])

















In [157]:



    


new_particle_list = []
for idx, count in enumerate(choices):
    while count > 0:
        new_particle_list.append(copy.copy(a[idx]))
        count -= 1
new_particle_list



    


















    
Out[157]:








[4, 5, 5, 8, 8, 8, 8, 9, 9, 9]

above implemented in montecarlo_localization.mcl_update()

Unit Tests:

Load and plot Wean Hall map

Test implementation





In [158]:



    


global_map = mcl.occupancy_map('data/map/wean.dat.gz')
mcl.draw_map_state(global_map, rotate=True)



    


















    
Out[158]:








<matplotlib.axes._subplots.AxesSubplot at 0x84a3caa208>

mcl.sample_list_by_weight()





In [159]:



    


toy_list = [l for l in 'abcd']
weights  = [10,5,1,0.1]

sample_list = [mcl.sample_list_by_weight(toy_list, weights) 
               for _ in range(100)]
print(sample_list[:10])
df = pd.DataFrame(sample_list)
print("\nValue Counts in first location:")
print(df[0].value_counts())



    


















    






[['a', 'c', 'd', 'b'], ['b', 'a', 'a', 'a'], ['a', 'a', 'a', 'b'], ['a', 'a', 'a', 'a'], ['a', 'b', 'b', 'a'], ['a', 'a', 'a', 'b'], ['b', 'a', 'a', 'a'], ['b', 'a', 'b', 'b'], ['a', 'a', 'a', 'a'], ['b', 'a', 'a', 'b']]

Value Counts in first location:
a    61
b    31
c     8
Name: 0, dtype: int64

mcl.robot_particle.sample_motion()





In [162]:



    


global_map = mcl.occupancy_map('data/map/wean.dat.gz')
logdata = mcl.load_log('data/log/robotdata1.log.gz')
logdata.head()



    


















    
Out[162]:










  
    

      
      type
      ts
      x
      y
      theta
      xl
      yl
      thetal
      1
      2
      ...
      171
      172
      173
      174
      175
      176
      177
      178
      179
      180
    

  
  
    

      0
      1
      0.025466
      -94.234001
      -139.953995
      -1.342158
      -88.567719
      -164.303391
      -1.342158
      66
      66
      ...
      71
      71
      71
      71
      71
      71
      70
      70
      70
      70
    

    

      1
      0
      0.025863
      -94.234001
      -139.953995
      -1.342158
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    

    

      2
      0
      0.079745
      -94.234001
      -139.953995
      -1.342158
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    

    

      3
      0
      0.139828
      -94.234001
      -139.953995
      -1.342158
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    

    

      4
      0
      0.200218
      -94.234001
      -139.953995
      -1.342158
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    

  



5 rows × 188 columns



















In [163]:



    


import time
fig, ax = plt.subplots(figsize=(10,10))
mcl.draw_map_state(global_map, ax=ax)
for _ in range(3):
    sensor = mcl.laser_sensor()
    particle = mcl.robot_particle(global_map, sensor)
    locations = np.array([particle.sample_motion(msg)
                          for msg in logdata.values])
    location_subset = locations[::50]
    for pose in location_subset[:, 0:4]:
        mcl.plot_particle(pose, ax, pass_pose=True)
plt.show()

Initialize valid particles uniformly on map

Particles are initialized uniformy over the entire 8000cm x 8000cm area with random heading, but are re-sampled if they end up in a grid cell which is not clear with high confidence (map value > 0.8).





In [164]:



    


particle_list = [mcl.robot_particle(global_map, sensor)
                 for _ in range(1000)]
mcl.draw_map_state(global_map, particle_list, rotate=True)
plt.show()

Animation of moving particle





In [165]:



    


import matplotlib.animation as animation

sensor = mcl.laser_sensor()
particle = mcl.robot_particle(global_map, sensor)
fig, ax = plt.subplots()

def init():    
    mcl.draw_map_state(global_map, ax=ax)

def animate(message):
    #plt.cla()
    #mcl.draw_map_state(global_map, ax=ax)
    particle.sample_motion(message)
    mcl.plot_particle(particle, ax)

ani = animation.FuncAnimation(fig, animate, logdata.values[::50], init_func=init,
    interval=50, blit=False, repeat=False)

plt.show()

Simple raycast in map





In [167]:



    


theta = 0
global_map = mcl.occupancy_map('data/map/wean.dat.gz')
fig, axes = plt.subplots(ncols=8, figsize=(12,4))
for ax in axes: 
    theta += np.pi/10
    x,y,dist = mcl.raycast_bresenham(2200, 4200, theta, global_map)
    ax = mcl.draw_map_state(global_map, ax=ax)
    ax.set_title("d: " + str(int(dist)) + 'cm')
    ax.set_ylim((4000,4500))
    ax.set_xlim((2000,2500))
    ax.xaxis.set_visible(False)
    ax.yaxis.set_visible(False)
    ax.scatter(2200, 4200, s=100)
    ax.plot([x, 2200],[y,4200], c='r')
    ax.scatter(x,y, s=40, c='r')
plt.show()



    


















    






c:\Anaconda3\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors == str('face'):

Raycasting each of 360 degrees around robot takes ~7.6ms per map location





In [168]:



    


%%timeit
for theta in np.linspace(0,2*np.pi, num=360):
    x,y,dist = mcl.raycast_bresenham(2200, 4200, theta, global_map)



    


















    






100 loops, best of 3: 7.34 ms per loop

Pre-calculate (cache) range values in map

Due to the static map, these values can be cached ahead of time (instead of re-computing for each particle)





In [179]:



    


%%time
def cache_map_ranges(theta_bins=120):
    slice_theta_rad = 2*np.pi/theta_bins
    slice_theta_deg = 360/theta_bins
    map_width, map_height = 800, 800

    coord_list = [(xidx, yidx) for yidx in range(map_height) 
                               for xidx in range(map_width)]
    range_array = np.zeros([map_width,map_height,theta_bins])
    raycast_degree_values = np.linspace(0,2*np.pi, num=theta_bins)
    # pre-calculate (cache) expected distance to wall for each theta bin, at each map location
    for xidx,yidx in coord_list:
        for idx, theta in enumerate(raycast_degree_values):
            _,_,dist = mcl.raycast_bresenham(xidx*10, yidx*10, theta, global_map, freespace_min_val=0.7)
            range_array[xidx,yidx,idx] = dist
    
    np.save('./data/range_array_{}bin'.format(theta_bins), range_array, allow_pickle=False)
    return range_array
raw_array = cache_map_ranges(theta_bins=120)  # Takes ~9 minutes with theta_bins=120 on core i5 laptop



    


















    






Wall time: 9min 35s

















In [182]:



    


range_array = np.load('./data/range_array_40bin.npy')
slice_theta_deg = 360/40
fig, axes = plt.subplots(ncols=3, nrows=2, figsize=(16, 6))
for idx, ax in enumerate(fig.axes):
    ax.imshow(range_array[:,:,idx].T, cmap=plt.cm.gray, interpolation='nearest',
                  origin='lower', extent=(0,8000,0,8000), aspect='equal')
    ax.set_ylim(2500,7500)
    ax.set_xlim(0,8000)
    ax.set_title("Theta = {:2.0f} deg".format(idx*slice_theta_deg))
fig.tight_layout()
fig.suptitle("Expected raycast distance at each map location, by angle.   White = high range.", size=16, y=0)



    


















    
Out[182]:








<matplotlib.text.Text at 0x84a6217e48>

Test particle stochastic movement

Very low error





In [89]:



    


fwd_err_pct, theta_err_pct = 0.01, 0.01
colors = ['r', 'g', 'b', 'm']

fig, ax = plt.subplots(figsize=(10,10))
plt.hold(True)
mcl.draw_map_state(global_map, ax=ax)
for idx in range(4):
    sensor = mcl.laser_sensor()
    particle = mcl.robot_particle(global_map, sensor,
                                  fwd_err_pct, theta_err_pct)
    locations = np.array([particle.sample_motion(msg)
                          for msg in logdata.values])
    location_subset = locations[::50]
    for pose in location_subset[:, 0:4]:
        mcl.plot_particle(pose, ax, pass_pose=True, color=colors[idx])
plt.show()

Theta error stdv = 10%





In [90]:



    


fwd_err_pct, theta_err_pct = 0.01, 0.1
colors = ['r', 'g', 'b', 'm']

fig, ax = plt.subplots(figsize=(10,10))
plt.hold(True)
mcl.draw_map_state(global_map, ax=ax)
for idx in range(4):
    sensor = mcl.laser_sensor()
    particle = mcl.robot_particle(global_map, sensor,
                                  fwd_err_pct, theta_err_pct)
    locations = np.array([particle.sample_motion(msg)
                          for msg in logdata.values])
    location_subset = locations[::50]
    for pose in location_subset[:, 0:4]:
        mcl.plot_particle(pose, ax, pass_pose=True, color=colors[idx])
plt.show()

Animate several moving particles





In [6]:



    


import matplotlib.animation as animation

wean_hall_map = mcl.occupancy_map('data/map/wean.dat')
logdata = mcl.load_log('data/log/robotdata1.log.gz')
logdata_scans = logdata.query('type > 0.1')

#Initialize 100 particles uniformly in valid locations on the map
laser = mcl.laser_sensor(stdv_cm=20, uniform_weight=0.2)
particle_list = [mcl.robot_particle(wean_hall_map, laser, log_prob_descale=100,
                                    sigma_fwd_pct=0.3, sigma_theta_pct=0.2)
                 for _ in range(300)]

# Pre-run first couple steps - hard to draw on screen
#for message in logdata_scans.values[:30:10]:
#    particle_list = mcl.mcl_update(particle_list, message, 
#                                    target_particles=300) # Update

class ParticleMap(object):
    def __init__(self, ax, global_map, particle_list):
        self.ax = ax
        self.global_map = global_map
        self.particle_list = particle_list
        mcl.draw_map_state(global_map, particle_list[::50], ax=self.ax)
        self.i = 1

    def update(self, message):
        self.particle_list = mcl.mcl_update(self.particle_list, message, 
                                            target_particles=300) # Update
        if self.i % 20 == 0:# Plot every 10th message
            plt.cla()        
            mcl.draw_map_state(self.global_map, self.particle_list, self.ax)
            #print(self.i, "  ", len(self.particle_list))
            print(pd.Series([p.weight for p in particle_list]).describe())
        self.i += 1

fig, ax = plt.subplots()
pmap = ParticleMap(ax, wean_hall_map, particle_list)

# pass a generator in "emitter" to produce data for the update func
ani = animation.FuncAnimation(fig, pmap.update, logdata_scans.values, interval=50,
                              blit=False, repeat=False)
plt.show()



    


















    






20    925
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
40    311
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
60    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
80    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
100    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
120    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
140    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
160    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
180    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
200    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
220    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
240    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
260    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
280    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
300    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
320    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
340    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
360    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
380    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
400    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
420    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
440    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
460    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
480    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
500    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
520    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
540    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
560    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64
580    300
count    3.000000e+04
mean     1.324679e-02
std      1.694024e-02
min      2.497719e-09
25%      5.407322e-05
50%      1.065434e-03
75%      2.798312e-02
max      1.238952e-01
dtype: float64

















In [22]:



    


np.concatenate([[1,2],[2,3]])



    


















    
Out[22]:








array([1, 2, 2, 3])

















In [3]:



    


%%prun
wean_hall_map = mcl.occupancy_map('data/map/wean.dat')
logdata = mcl.load_log('data/log/robotdata1.log.gz')

#Initialize 100 particles uniformly in valid locations on the map
laser = mcl.laser_sensor(stdv_cm=100, uniform_weight=0.3)
particle_list = [mcl.robot_particle(wean_hall_map, laser, log_prob_descale=70,
                                    sigma_fwd_pct=0.1, sigma_theta_pct=0.1)
                 for _ in range(10000)]
for message in logdata.values[:100]:
    particle_list = mcl.mcl_update(particle_list, message)



    


















    






 
















In [24]:



    


mcl.draw_map_state(wean_hall_map, particle_list)
plt.show()

Re-weight per sensor model





In [35]:



    


%matplotlib inline
theta = 0
global_map = mcl.occupancy_map('data/map/wean.dat.gz')
fig, axes = plt.subplots(ncols=8, figsize=(12,4))
for ax in axes: 
    theta += np.pi/10
    x,y,dist = mcl.raycast_bresenham(2200, 4200, theta, global_map)
    ax = mcl.draw_map_state(global_map, ax=ax)
    ax.set_title("d: " + str(int(dist)) + 'cm')
    ax.set_ylim((4000,4500))
    ax.set_xlim((2000,2500))
    ax.xaxis.set_visible(False)
    ax.yaxis.set_visible(False)
    ax.scatter(2200, 4200, s=100)
    ax.plot([x, 2200],[y,4200], c='r')
    ax.scatter(x,y, s=40, c='r')
plt.show()



    


















    






c:\Anaconda3\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors == str('face'):

View likelihood of two similar measurements





In [36]:



    


wean_hall_map = mcl.occupancy_map('data/map/wean.dat')
logdata = mcl.load_log('data/log/robotdata1.log.gz')
laser = mcl.laser_sensor(stdv_cm=100, uniform_weight=0.1)
particle = mcl.robot_particle(wean_hall_map, laser, sigma_fwd_pct=0.4, sigma_theta_pct=0.1)
range_array_120 = np.load('./data/range_array_120bin.npy')
logdata = mcl.load_log('data/log/robotdata1.log.gz')



    











In [37]:



    


rng_far = (logdata.values[0,8:188:3])
rng1 = particle.global_map.ranges_180(2200, 4200, 2*np.pi/40)
rng2 = particle.global_map.ranges_180(2200, 4200, 0)

Measurement probabilities of poor match match





In [38]:



    


sample_probs_exact_match = laser.measurement_probabilities(rng2, rng2)
sample_probs_poor_match = laser.measurement_probabilities(rng_far, rng2)
print("Dist Error: ", rng_far - rng2)
print("Probabilities: ",sample_probs_poor_match)



    


















    






Dist Error:  [  -74.35668848   -74.35668848   -75.42135624   -77.17821063   -89.24174696
   -91.11388301   -94.55494421   -98.52945357   -97.52945357  -103.
   325.72243623   294.98924762   252.90627288   203.50566759   152.72583002
   108.04902432    87.81487828    71.97058983    43.55589796     7.15290347
   -73.21422387  -119.18546143  -211.96356141  -323.83275543  -544.43501271
  -908.86022959 -1923.17275369 -3216.72700079  -979.97423484  -609.          -677.
  -451.70273919  -170.89721644  -164.10561809  -112.94117082    12.38490029
    65.103799      87.86792084   100.11705772   125.           150.35662478
   169.77794898   188.98529491   211.93751525   251.58405421   -21.13708499
   -17.30145813   -14.           -17.           -27.16653826   -23.95630141
   -21.48857802   -22.48857802   -20.86832981   -21.86832981   -32.40306509
   -62.41664064   -60.52946438   -59.3840481    -60.3840481 ]
Probabilities:  [  1.25000000e-05   1.25000000e-05   1.25000000e-05   1.25000000e-05
   1.25000000e-05   1.25000000e-05   1.25000000e-05   1.25000000e-05
   1.25000000e-05   1.25000000e-05   1.25000000e-05   1.25000000e-05
   1.25000000e-05   1.25000000e-05   1.25000000e-05   1.25000000e-05
   1.25000000e-05   1.25000002e-05   1.52265721e-05   2.78129163e-02
   1.25000001e-05   1.25000000e-05   1.25000000e-05   1.25000000e-05
   1.25000000e-05   1.25000000e-05   1.25000000e-05   1.25000000e-05
   1.25000000e-05   1.25000000e-05   1.25000000e-05   1.25000000e-05
   1.25000000e-05   1.25000000e-05   1.25000000e-05   1.66880298e-02
   1.25000225e-05   1.25000000e-05   1.25000000e-05   1.25000000e-05
   1.25000000e-05   1.25000000e-05   1.25000000e-05   1.25000000e-05
   1.25000000e-05   3.85832955e-03   8.05040816e-03   1.34879719e-02
   8.47691696e-03   9.09021621e-04   2.04923748e-03   3.58075332e-03
   2.87641747e-03   4.08165432e-03   3.29876577e-03   2.00950106e-04
   1.25001246e-05   1.25003974e-05   1.25007898e-05   1.25004340e-05]

Measurement probabilities of good match match





In [39]:



    


sample_probs_good_match = laser.measurement_probabilities(rng1, rng2)
print("\nDist Error: ", rng1 - rng2)
print("Probabilities: ",sample_probs_good_match)



    


















    






Dist Error:  [  2.82152216e+00   1.48850585e+01   1.66925268e+01   1.83767336e+01
   1.02877066e+01   7.41557056e+00   8.44505579e+00   1.47481102e+01
   2.04812988e+01   2.20937271e+01   1.82167686e+01   4.02634176e+01
   6.28572486e+01   7.16907893e+01   4.57552402e+01   3.34931264e+01
   4.36619748e+01   9.91848137e+01   1.18741359e+02   1.80116465e+02
   2.29618532e+02   4.10249551e+02   6.82896668e+02   1.58734000e+03
   2.66229199e+03   6.51140052e+01  -1.31817275e+03  -2.60972700e+03
  -6.01271496e+02  -4.37102784e+02  -5.14894382e+02  -3.34761568e+02
  -1.73282117e+02  -1.43209417e+02  -1.12809092e+02  -7.17321574e+01
  -4.18962010e+01  -4.04887039e+01  -4.36608913e+01  -3.39852949e+01
  -2.45808905e+01  -2.38061052e+01  -2.28776201e+01  -2.17610266e+01
  -2.04159458e+01  -1.31370850e+01   1.86508014e+00   2.95630141e+00
  -1.51142198e+00  -9.67796025e+00  -8.08797160e+00  -3.62024821e+00
   5.91448707e+00   3.85483108e+01   3.66611346e+01   2.59809830e+01
  -3.03259254e+00  -1.52946438e+00   0.00000000e+00   1.10373081e+01]
Probabilities:  [  3.45161874e-02   1.18710166e-02   8.92690838e-03   6.64743885e-03
   2.11634876e-02   2.72860591e-02   2.51478771e-02   1.21140962e-02
   4.42064133e-03   3.13991644e-03   6.84450261e-03   2.33364173e-05
   1.25000945e-05   1.25000002e-05   1.35211367e-05   1.44088398e-04
   1.51033165e-05   1.25000000e-05   1.25000000e-05   1.25000000e-05
   1.25000000e-05   1.25000000e-05   1.25000000e-05   1.25000000e-05
   1.25000000e-05   1.25000223e-05   1.25000000e-05   1.25000000e-05
   1.25000000e-05   1.25000000e-05   1.25000000e-05   1.25000000e-05
   1.25000000e-05   1.25000000e-05   1.25000000e-05   1.25000002e-05
   1.80409620e-05   2.23942194e-05   1.51045484e-05   1.23955462e-04
   1.76277034e-03   2.12361851e-03   2.63451637e-03   3.37659755e-03
   4.47994620e-03   1.51618273e-02   3.52982259e-02   3.43821032e-02
   3.55095344e-02   2.24909386e-02   2.59008928e-02   3.36398644e-02
   3.01558806e-02   3.38012355e-05   5.58121188e-05   1.24102436e-03
   3.43036756e-02   3.54997980e-02   3.59173052e-02   1.95387611e-02]

Full probabilities of a complete scan (perfect, good, poor)





In [41]:



    


laser.full_scan_log_prob(sample_probs_exact_match)



    


















    
Out[41]:








0.0

















In [42]:



    


laser.full_scan_log_prob(sample_probs_good_match)



    


















    
Out[42]:








-269.16528146389032

















In [43]:



    


laser.full_scan_log_prob(sample_probs_poor_match)



    


















    
Out[43]:








-402.36610332642528

















In [44]:



    


rng = 10
perfect_match_probs = laser.measurement_probabilities(np.array(list(range(rng))), np.array(list(range(rng))))
scaling_factor = 1/perfect_match_probs[0]
scaling_factor



    


















    
Out[44]:








27.841732374568785

















In [173]:



    






    


















    
Out[173]:








array([ 0.04570777,  0.00318848,  0.00157496,  0.00076577,  0.01346549])

















In [ ]:



    






    











In [ ]:



    






    











In [ ]:



    






    











In [ ]:



    






    











In [ ]:



    






    











In [ ]:



    






    











In [ ]:



    






    











In [ ]:



    






    











In [ ]:

Control rate of particle depletion





In [24]:



    


%matplotlib inline
wean_hall_map = mcl.occupancy_map('data/map/wean.dat')
logdata = mcl.load_log('data/log/robotdata1.log.gz')

#Initialize 100 particles uniformly in valid locations on the map
laser = mcl.laser_sensor(stdv_cm=20, uniform_weight=0.2)
particle_list = [mcl.robot_particle(wean_hall_map, laser, log_prob_descale=50,
                                    sigma_fwd_pct=0.3, sigma_theta_pct=0.2)
                 for _ in range(30000)]
scan_data_gen = (msg for msg in logdata.query('type > 0.1').values)



    











In [25]:



    


fig, ax = plt.subplots(figsize=(40,40))
mcl.draw_map_state(wean_hall_map, particle_list[::20], ax=ax)



    


















    
Out[25]:








<matplotlib.axes._subplots.AxesSubplot at 0x575fa38f60>










    
























In [26]:



    


new_particle_list = mcl.mcl_update(particle_list, next(scan_data_gen))



    











In [27]:



    


weights = pd.Series([p.weight for p in new_particle_list])
weights.describe()



    


















    
Out[27]:








count    27131.000000
mean         0.000189
std          0.000193
min          0.000071
25%          0.000084
50%          0.000124
75%          0.000220
max          0.006939
dtype: float64

















In [28]:



    


new_particle_list = mcl.mcl_update(new_particle_list, next(scan_data_gen))



    











In [29]:



    


weights = pd.Series([p.weight for p in new_particle_list])
weights.describe()



    


















    
Out[29]:








count    16422.000000
mean         0.000126
std          0.000511
min          0.000003
25%          0.000022
50%          0.000058
75%          0.000130
max          0.030840
dtype: float64

















In [30]:



    


fig, ax = plt.subplots(figsize=(40,40))
mcl.draw_map_state(wean_hall_map, new_particle_list, ax=ax)



    


















    
Out[30]:








<matplotlib.axes._subplots.AxesSubplot at 0x5811efd438>










    
























In [31]:



    


new_particle_list = mcl.mcl_update(new_particle_list, next(scan_data_gen))



    











In [32]:



    


pd.Series([p.weight for p in new_particle_list]).describe()



    


















    
Out[32]:








count    6.985000e+03
mean     3.432880e-04
std      3.318940e-03
min      1.538964e-07
25%      3.471401e-05
50%      8.884922e-05
75%      2.022310e-04
max      1.417625e-01
dtype: float64

















In [33]:



    


fig, ax = plt.subplots(figsize=(40,40))
mcl.draw_map_state(wean_hall_map, new_particle_list, ax=ax)



    


















    
Out[33]:








<matplotlib.axes._subplots.AxesSubplot at 0x5811fcd0b8>










    
























In [34]:



    


for _ in range(20):
    new_particle_list = mcl.mcl_update(new_particle_list, next(scan_data_gen))
fig, ax = plt.subplots(figsize=(40,40))
mcl.draw_map_state(wean_hall_map, new_particle_list, ax=ax)



    


















    
Out[34]:








<matplotlib.axes._subplots.AxesSubplot at 0x5811f2f358>










    
























In [65]:



    


len(new_particle_list)



    


















    
Out[65]:








300

















In [ ]:



    


new_particle_list



    











In [ ]:



    






    











In [276]:



    


p = mcl.robot_particle(wean_hall_map, laser)
print(p.pose)



    


















    






[  6.58390449e+03   6.01333879e+03   4.06236805e+00]

















In [300]:



    


p.new_pose_from_sample_error()
print(p.pose[0])
print(p.pose[1])
p.pose[2]



    


















    






6598.08194782
6003.75199296










    
Out[300]:








4.358746777868638

















In [ ]:



    


new_particle_list = mcl.mcl_update(new_particle_list, next(scan_data_gen))



    











In [403]:



    


particle_list[0].



    


















    
Out[403]:








<montecarlo_localization.robot_particle at 0xf0a633e320>

















In [ ]:



    






    











In [414]:



    


for p in new_particle_list:
    print(p.pose)



    


















    






[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
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[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]
[  1.56937407e+03   4.37230654e+03  -1.65914312e+00]
[  5.53365050e+03   3.53610856e+03  -1.68081983e+00]

















In [370]:



    


max([p.weight for p in particle_list])



    


















    
Out[370]:








0.00010000000000000662

















In [371]:



    


len([p.weight for p in particle_list])



    


















    
Out[371]:








10000

















In [ ]:



    






    











In [333]:



    






    


















    
Out[333]:










  
    

      
      type
      ts
      x
      y
      theta
      xl
      yl
      thetal
      1
      2
      ...
      171
      172
      173
      174
      175
      176
      177
      178
      179
      180
    

  
  
    

      0
      1
      0.025466
      -94.234001
      -139.953995
      -1.342158
      -88.567719
      -164.303391
      -1.342158
      66
      66
      ...
      71
      71
      71
      71
      71
      71
      70
      70
      70
      70
    

    

      5
      1
      0.204230
      -94.234001
      -139.953995
      -1.342158
      -88.567719
      -164.303391
      -1.342158
      66
      66
      ...
      71
      71
      71
      71
      70
      70
      70
      70
      70
      70
    

    

      9
      1
      0.394368
      -94.234001
      -139.953995
      -1.342158
      -88.567719
      -164.303391
      -1.342158
      66
      66
      ...
      71
      71
      70
      70
      70
      70
      71
      70
      70
      70
    

    

      13
      1
      0.584142
      -94.234001
      -139.953995
      -1.342158
      -88.567719
      -164.303391
      -1.342158
      66
      66
      ...
      71
      71
      70
      71
      70
      71
      70
      70
      70
      70
    

    

      17
      1
      0.784171
      -94.234001
      -139.953995
      -1.342158
      -88.567719
      -164.303391
      -1.342158
      66
      66
      ...
      71
      71
      71
      71
      70
      71
      70
      70
      70
      70
    

  



5 rows × 188 columns



















In [334]:



    


%%prun
wean_hall_map = mcl.occupancy_map('data/map/wean.dat')
logdata = mcl.load_log('data/log/robotdata1.log.gz')
laser = mcl.laser_sensor(stdv_cm=100, uniform_weight=0.4)
particle_list = [mcl.robot_particle(wean_hall_map, laser, 
                 sigma_fwd_pct=0.4, sigma_theta_pct=0.1)
                 for _ in range(10000)]
for message in logdata.values:
    particle_list = mcl.mcl_update(particle_list, message)



    


















    






---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-334-46019e95ea72> in <module>()
----> 1 get_ipython().run_cell_magic('prun', '', "wean_hall_map = mcl.occupancy_map('data/map/wean.dat')\nlogdata = mcl.load_log('data/log/robotdata1.log.gz')\nlaser = mcl.laser_sensor(stdv_cm=100, uniform_weight=0.4)\nparticle_list = [mcl.robot_particle(wean_hall_map, laser, \n                 sigma_fwd_pct=0.4, sigma_theta_pct=0.1)\n                 for _ in range(10000)]\nfor message in logdata.values:\n    particle_list = mcl.mcl_update(particle_list, message)")

c:\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py in run_cell_magic(self, magic_name, line, cell)
   2291             magic_arg_s = self.var_expand(line, stack_depth)
   2292             with self.builtin_trap:
-> 2293                 result = fn(magic_arg_s, cell)
   2294             return result
   2295 

c:\Anaconda3\lib\site-packages\IPython\core\magics\execution.py in prun(self, parameter_s, cell)

c:\Anaconda3\lib\site-packages\IPython\core\magic.py in <lambda>(f, *a, **k)
    191     # but it's overkill for just that one bit of state.
    192     def magic_deco(arg):
--> 193         call = lambda f, *a, **k: f(*a, **k)
    194 
    195         if callable(arg):

c:\Anaconda3\lib\site-packages\IPython\core\magics\execution.py in prun(self, parameter_s, cell)
    273             arg_str += '\n' + cell
    274         arg_str = self.shell.input_splitter.transform_cell(arg_str)
--> 275         return self._run_with_profiler(arg_str, opts, self.shell.user_ns)
    276 
    277     def _run_with_profiler(self, code, opts, namespace):

c:\Anaconda3\lib\site-packages\IPython\core\magics\execution.py in _run_with_profiler(self, code, opts, namespace)
    295         prof = profile.Profile()
    296         try:
--> 297             prof = prof.runctx(code, namespace, namespace)
    298             sys_exit = ''
    299         except SystemExit:

c:\Anaconda3\lib\cProfile.py in runctx(self, cmd, globals, locals)
     98         self.enable()
     99         try:
--> 100             exec(cmd, globals, locals)
    101         finally:
    102             self.disable()

<string> in <module>()

C:\Users\Erik\Desktop\work\robostats\robostats_mcl\montecarlo_localization.py in mcl_update(particle_list, msg, min_particles)
     25         particle.sample_motion(msg)
     26         if msg[0] > 0.1: # This message has a laser scan
---> 27             particle.update_measurement_likelihood(msg)  # Update weight
     28 
     29     # SECOND: Renormalize particle weights - often get too small

C:\Users\Erik\Desktop\work\robostats\robostats_mcl\montecarlo_localization.py in update_measurement_likelihood(self, laser_msg)
    175         expected_measurements = self.global_map.ranges_180(*self.pose)
    176 
--> 177         beam_probabilities = self.sensor.measurement_probabilities(subsampled_measurements,
    178                                                                    expected_measurements)
    179         single_scan_log_prob = self.sensor.full_scan_log_prob(beam_probabilities)

AttributeError: 'robot_particle' object has no attribute 'sensor'












 135105098 function calls (135103848 primitive calls) in 415.633 seconds

   Ordered by: internal time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
   905268  179.985    0.000  179.985    0.000 montecarlo_localization.py:85(<listcomp>)
 54316080   59.674    0.000   59.674    0.000 montecarlo_localization.py:89(rads_to_bucket_id)
  2718627   26.278    0.000   32.328    0.000 montecarlo_localization.py:165(new_pose_from_log_delta)
   905268   26.027    0.000  352.293    0.000 montecarlo_localization.py:136(update_measurement_likelihood)
   905268   18.846    0.000   78.519    0.000 montecarlo_localization.py:99(<listcomp>)
9188368/9188365   13.292    0.000   13.292    0.000 {built-in method numpy.core.multiarray.array}
  2849599   11.917    0.000   11.917    0.000 montecarlo_localization.py:192(position_valid)
  1810536   10.112    0.000   15.713    0.000 blas.py:221(_get_funcs)




In [ ]:



    


mcl.draw_map_state(wean_hall_map, particle_list)
plt.show()

Content source: sjobeek/robostats_mcl

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