Use Python 2.7
/fmcl/localization_status
header:
seq: 2357667
stamp:
secs: 1586466240
nsecs: 859080339
frame_id: ''
localization_valid: True
localization_bad: False
mislocalization_cause: ''
delay_mislocalization_time: 30.0
delay_mislocalization_travel: 5.0
legacy_localization_score: 0.442291858129
legacy_localization_mean_score: 0.707680712006
patch_map_score: -1.31935665953
patch_map_travel: 2742.83326072
patch_map_score_min: -1.63
stddev_position: 0.096996682294
stddev_orientation: 0.0221780192784
stddev_travel: 2744.44020139
/fmcl/localization_score
header:
seq: 2357670
stamp:
secs: 1586466240
nsecs: 859080339
frame_id: ''
pose:
position:
x: -26.5091686249
y: 8.25257396698
z: 0.0
orientation:
x: 0.0
y: 0.0
z: -0.329938150857
w: 0.944002551166
scores:
importance: 0.824907920561
likelihood: 0.824907920561
clear: 1.0
dynamic: 1.0
best_possible:
importance: 1.77050469171
likelihood: 1.77050469171
clear: 1.0
dynamic: 1.0
worst_possible:
importance: 0.075
likelihood: 0.075
clear: 1.0
dynamic: 1.0
ratio:
importance: 0.442291858129
likelihood: 0.442291858129
clear: 1.0
dynamic: 1.0
fmcl/pose
header:
seq: 2357670
stamp:
secs: 1586466240
nsecs: 859080339
frame_id: ''
pose:
pose:
position:
x: -26.5091686249
y: 8.25257396698
z: 0.0
orientation:
x: 0.0
y: 0.0
z: -0.329938150857
w: 0.944002551166
covariance: [0.002960541175873165, 0.0009347208515437376, 0.0, 0.0, 0.0, 0.0, 0.0009347208515437376, 0.006447815200170211, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0004918645391128115]
/fmcl/particlecloud
-
position:
x: -31.6127815247
y: 23.0928459167
z: 0.0
orientation:
x: 0.0
y: 0.0
z: 0.422332406044
w: 0.906441032887
-
position:
x: -31.4858016968
y: 23.1055259705
z: 0.0
orientation:
x: 0.0
y: 0.0
z: 0.41169410944
w: 0.911322116852
- repeat...