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sensors.yaml
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sensors.yaml
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# AMZ-Driverless
# Copyright (c) 2018 Authors:
# - Juraj Kabzan <kabzanj@gmail.com>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# All positions (pos) are with respect to chassis origin
stream_track:
enabled: true
markers: "/fssim/track"
sensors:
lidar:
enabled: false
topic: lidar/raw
pos:
x: 0.75
y: 0.0
z: 0.15
velocity:
noise_vx_sigma: 0.01 # Sigma of gaussian noise on respectible velocity estimate
noise_vy_sigma: 0.01 # Sigma of gaussian noise on respectible velocity estimate
noise_r_sigma: 0.01 # Sigma of gaussian noise on respectible velocity estimate
lidar_pipeline:
enabled: true
node_name: lidar_pipeline # This name MUST BE the SAME to this section yaml file
topic_name: /lidar/cones # Where should the cones be published
overwrite:
enabled: true
frame: fssim/vehicle/base_link # Frame the cones are represented in
rate: 10 # Publishing frequency [Hz]
delay:
time: 0.2 # Delay [s]
noise_sigma: 0.002 # Additive gassian noise on delay
cut_cones_below_x: -1 # Cut cones that are further than this distance in x car axis
observation_radius: 15.0 # Below this threshold we can observe cones [m]
distance_dependent_observation: 100.0 # The probability of observation decreases with factor = -1/distance_dependent_misclass * r + 1
observation_likelihood_left: 1.0 # Likelihood of observing left [0..1]
observation_likelihood_right: 1.0 # Likelihood of observing right [0..1]
observation_likelihood_orange: 1.0 # Likelihood of observing right [0..1]
color_observation_radius: 10.0 # We can obser color only below this threshild [m]
distance_dependent_misclass: 200.0 # The probability of color observation decreases with factor = -1/distance_dependent_misclass * r + 1
likelihood_yellow: 0.8 # Likelihood of observic color of cones left [0..1]
likelihood_blue: 0.8 # Likelihood of observic color of cones right [0..1]
likelihood_orange: 0.8 # Likelihood of observic color of cones right [0..1]
gaussian_noise_xy_mu: 0.0 # Mean of Gaussian noise on [x,y] cone position
gaussian_noise_xy_sigma: 0.0 # Variance of Gaussian noise on [x,y] cone position
gaussian_noise_mu_radial: 0.0 # Mean of Gaussian noise on r (cone position is described with [r,\theta]) cone position (radial coordinates)
gaussian_noise_sigma_radial: 0.05 # Variance of Gaussian noise on r (cone position is described with [r,\theta]) cone position (radial coordinates)
gaussian_noise_mu_angular: 0.00 # Mean of Gaussian noise on \theta (cone position is described with [r,\theta]) cone position (radial coordinates)
gaussian_noise_sigma_angular: 0.007 # Variance of Gaussian noise on r (cone position is described with [r,\theta]) cone position (radial coordinates)
camera_pipeline:
enabled: true
node_name: camera_pipeline
topic_name: /camera/cones
overwrite:
enabled: true
frame: fssim/vehicle/base_link # Frame the cones are represented in
rate: 10 # Publishing frequency [Hz]
delay:
time: 0.2 # Delay [s]
noise_sigma: 0.002 # Additive gassian noise on delay
cut_cones_below_x: -1 # Cut cones that are further than this distance in x car axis
observation_radius: 10.0 # Below this threshold we can observe cones [m]
distance_dependent_observation: 100.0 # The probability of observation decreases with factor = -1/distance_dependent_misclass * r + 1
observation_likelihood_left: 1.0 # Likelihood of observing left [0..1]
observation_likelihood_right: 1.0 # Likelihood of observing right [0..1]
observation_likelihood_orange: 1.0 # Likelihood of observing right [0..1]
color_observation_radius: 10.0 # We can obser color only below this threshild [m]
distance_dependent_misclass: 200.0 # The probability of color observation decreases with factor = -1/distance_dependent_misclass * r + 1
likelihood_yellow: 0.99 # Likelihood of observic color of cones left [0..1]
likelihood_blue: 0.99 # Likelihood of observic color of cones right [0..1]
likelihood_orange: 0.99 # Likelihood of observic color of cones right [0..1]
gaussian_noise_xy_mu: 0.0 # Mean of Gaussian noise on [x,y] cone position
gaussian_noise_xy_sigma: 0.0 # Variance of Gaussian noise on [x,y] cone position
gaussian_noise_mu_radial: 0.0 # Mean of Gaussian noise on r (cone position is described with [r,\theta]) cone position (radial coordinates)
gaussian_noise_sigma_radial: 0.2 # Variance of Gaussian noise on r (cone position is described with [r,\theta]) cone position (radial coordinates)
gaussian_noise_mu_angular: 0.00 # Mean of Gaussian noise on \theta (cone position is described with [r,\theta]) cone position (radial coordinates)
gaussian_noise_sigma_angular: 0.007 # Variance of Gaussian noise on r (cone position is described with [r,\theta]) cone position (radial coordinates)