Commit 2edcd150 authored by czb5793's avatar czb5793
Browse files

modify the configuration files

parent 0c763868
......@@ -34,7 +34,7 @@ robot:
motor:
noise:
# Standard deviation of the left wheel's velocity in meters/second while executing a motion command.
left_velocity: 0.05 # 0.005, 0.05
left_velocity: 0.05
# Standard deviation of the right wheel's velocity in meters/second while executing a motion command.
right_velocity: 0.05
......@@ -48,8 +48,8 @@ robot:
min_read_value: 18
# Value that is read at minimum range
max_read_value: 3960
# Standard deviation of the distance from the sensors to the detected landmark in meters.
noise: 0.005 # 0.0001 0.005
# Standard deviation of Gaussian noise in meters.
noise: 0.005
# Specificies the poses and number of sensors
# Each pose is composed of x position, y position and angle in degrees
poses: [[-0.038, 0.048, 128],
......@@ -92,14 +92,15 @@ viewer:
map:
# Configures the generated obstacles
obstacle:
# Configures features of maps
feature:
# Determines whether octagon obstacles shall be generated
# Determines whether features of the maps shall be generated
enabled: true
# Radius of feature points
radius: 0.04
# Density of features
density: 0.15
# The following attributes are used if rectangles are disable
# The following attributes are used if rectangular obstacles are disabled
# Minimum amount of generated obstacles
min_count: 80
# Maximum amount of generated obstacles
......@@ -135,7 +136,7 @@ map:
# Minimum distance to origin
min_distance: 0
# Maximum distance to origin
max_distance: 3.5
max_distance: 5.0
# Minimum distance to all obstacles
min_clearance: 0.2
......@@ -147,9 +148,9 @@ control:
danger_distance: 0.06 # 0.06
# If a sensor measures a distance smaller than the caution distance, the robot will follow the wall of the obstacle
# Set to danger_distance to disable wall following, since the map contains small, circle-like objects, where wall following can lead to looping around an object
caution_distance: 0.06 # a. 0.06; b. 0.15
caution_distance: 0.15 # a. 0.06; b. 0.15
# Criterion for stopping the following of a wall
progress_epsilon: 0.05 # a. 0.05; b. 0.2
progress_epsilon: 0.2 # a. 0.05; b. 0.2
slam:
# The amount of variables that describe the robot's state
......@@ -172,15 +173,15 @@ slam:
# Configures the motion noise. The values are currently empirically chosen.
motion_noise:
# Standard deviation of the robots x-coordinate in meters after executing a motion command.
x: 0.0002 # 0.0005
x: 0.0002
# Standard deviation of the robots y-coordinate in meters after executing a motion command.
y: 0.0002 # 0.0005
y: 0.0002
# Standard deviation of the robots angle in degrees after executing a motion command.
theta: 0.1
# Configures the sensor noise. The values are currently empirically chosen.
sensor_noise:
# Standard deviation of the detected distance in meters 0.2 0.01
detected_distance: 0.2 # 0.008
detected_distance: 0.2
# Standard deviation of the detected angle in degrees
detected_angle: 30
fast_slam:
......@@ -193,13 +194,13 @@ slam:
# Configures the motion noise. The values are currently empirically chosen.
motion_noise:
# Standard deviation of the motion command's translational velocity in m/s.
translational_velocity: 0.01 #0.005
translational_velocity: 0.01
# Standard deviation of the motion command's rotational velocity in rad/s.
rotational_velocity: 0.0001 #0.005
rotational_velocity: 0.0001
# Configures the sensor noise. The values are currently empirically chosen.
sensor_noise:
# Standard deviation of the detected distance in meters 0.2 0.01
detected_distance: 0.2 # 0.008
detected_distance: 0.2
# Standard deviation of the detected angle in degrees
detected_angle: 30
graph_based_slam:
......@@ -215,29 +216,31 @@ slam:
frontend_pose_density: 0.1
# number of fixed vertexes while the graph optimization
num_fixed_vertexes: 20
# draw trajectory
draw_trajectory: true
# sparse solver: cholesky or spsolve
# draw trajectory on the frame
draw_trajectory: false
# sparse solver: cholesky or spsolve.
# For the faster sparse solver cholesky, users have to install the scikit-sparse library.
solver: "cholesky"
# Configures the motion noise. The values are currently empirically chosen.
motion_noise:
# Standard deviation of the robots x-coordinate in meters after executing a motion command.
x: 0.01 # 0.005 0.01
x: 0.01
# Standard deviation of the robots y-coordinate in meters after executing a motion command.
y: 0.01 # 0.005
y: 0.01
# Standard deviation of the robots angle in degrees after executing a motion command in degrees.
theta: 1.0 #0.09
theta: 1.0
# Configures the sensor noise. The values are currently empirically chosen.
sensor_noise:
#x: 0.02 # 0.02 constraint range-bearing
#y: 0.5236 # rad/s
x: 0.02 # 0.02 constraint x-y
y: 0.02 # 0.02
x: 0.02
y: 0.02
# Configures the evaluation of the SLAM algorithms
evaluation:
# Determines whether the accuracy of the generated maps shall be evaluated
enabled: true
# The raw data will be recorded during the a simulation. After clicking the
# button Plot Slam Evaluation, raw data will be stored in
# the file \scripts as two csv files, while can be analysed through jupyter notebook.
enabled: false
# Determines the interval of when the accuracy of the generated maps is calculated
# A low interval (for example 1) causes performance problems
interval: 18
......@@ -245,7 +248,7 @@ slam:
# if true, landmark identifiers will be associated, otherwise by minimum distance
associate_id: true
# Configures the 2D grid map of the occupancy grid mapping algorithm
# Configures the 2D occupancy grid mapping algorithm
mapping:
# Determines whether the mapping algorithm shall be executed
enabled: true
......
......@@ -34,7 +34,7 @@ robot:
motor:
noise:
# Standard deviation of the left wheel's velocity in meters/second while executing a motion command.
left_velocity: 0.005 # 0.005
left_velocity: 0.005
# Standard deviation of the right wheel's velocity in meters/second while executing a motion command.
right_velocity: 0.005
......@@ -48,8 +48,8 @@ robot:
min_read_value: 18
# Value that is read at minimum range
max_read_value: 3960
# Standard deviation of the distance from the sensors to the detected landmark.
noise: 0.0001 # 0.0001
# Standard deviation of Gaussian noise in meters.
noise: 0.0001
# Specificies the poses and number of sensors
# Each pose is composed of x position, y position and angle in degrees
poses: [[-0.038, 0.048, 128],
......@@ -94,13 +94,13 @@ map:
obstacle:
# Configures features
feature:
# Determines whether octagon obstacles shall be generated
# Determines whether features of the map shall be generated
enabled: true
# Radius of feature points
radius: 0.04
# Density of features
density: 0.15
# The following attributes are used if rectangles are disable
# The following attributes are used if rectangular obstacles are disabled
# Minimum amount of generated obstacles
min_count: 40
# Maximum amount of generated obstacles
......@@ -144,10 +144,10 @@ control:
# If robot is closer than this distance to the goal, it is considered as reached
goal_reached_distance: 0.05
# If a sensor measures a distance smaller than the danger distance, the robot immediately starts moving into the opposite direction
danger_distance: 0.06 # 0.06
danger_distance: 0.1 # 0.06
# If a sensor measures a distance smaller than the caution distance, the robot will follow the wall of the obstacle
# Set to danger_distance to disable wall following, since the map contains small, circle-like objects, where wall following can lead to looping around an object
caution_distance: 0.06 # 0.06
caution_distance: 0.1 # 0.06
# Criterion for stopping the following of a wall
progress_epsilon: 0.05 # 0.05
......@@ -164,6 +164,7 @@ slam:
# Determines whether landmark-identifiers is used
# It determines whether correspondences of landmarks are given, i.e. identifies of landmarks are given
feature_detector: true
ekf_slam:
# Determines whether the EKF SLAM algorithm shall be executed
enabled: true
......@@ -172,9 +173,9 @@ slam:
# Configures the motion noise. The values are currently empirically chosen.
motion_noise:
# Standard deviation of the robots x-coordinate in meters after executing a motion command.
x: 0.01 # 0.0005
x: 0.01
# Standard deviation of the robots y-coordinate in meters after executing a motion command.
y: 0.01 # 0.0005
y: 0.01
# Standard deviation of the robots angle in degrees after executing a motion command.
theta: 1
# Configures the sensor noise. The values are currently empirically chosen.
......@@ -193,13 +194,13 @@ slam:
# Configures the motion noise. The values are currently empirically chosen.
motion_noise:
# Standard deviation of the motion command's translational velocity in m/s. # 0.02,0.015
translational_velocity: 0.005 #0.005
translational_velocity: 0.005
# Standard deviation of the motion command's rotational velocity in rad/s.
rotational_velocity: 0.003 #0.005 0.001
rotational_velocity: 0.005
# Configures the sensor noise. The values are currently empirically chosen.
sensor_noise:
# Standard deviation of the detected distance in meters 0.2 0.01
detected_distance: 0.2 # 0.008
detected_distance: 0.2
# Standard deviation of the detected angle in degrees
detected_angle: 30
......@@ -209,7 +210,7 @@ slam:
# The euclidean distance threshold [m] used in data association.
# If the distance between the estimated landmarks via slam and via actual measurement
# larger than the threshold, start backend
distance_threshold: 0.1 #0.2
distance_threshold: 0.1
# The timestep interval of executing the frontend part in simulation cycles.
frontend_interval: 5
# Pose density of frontend, meaning the minimum distance [m] between the current pose and the last pose.
......@@ -219,6 +220,7 @@ slam:
# draw trajectory on the frame
draw_trajectory: false
# sparse solver: cholesky or spsolve
# For the faster sparse solver cholesky, users have to install the scikit-sparse library.
solver: "cholesky"
# Configures the motion noise. The values are currently empirically chosen.
motion_noise:
......@@ -227,7 +229,7 @@ slam:
# Standard deviation of the robots y-coordinate in meters after executing a motion command.
y: 0.005
# Standard deviation of the robots angle in degrees after executing a motion command in degrees.
theta: 1.0 #0.09
theta: 1.0
# Configures the sensor noise. The values are currently empirically chosen.
sensor_noise:
x: 0.02
......@@ -236,7 +238,10 @@ slam:
# Configures the evaluation of the SLAM algorithms
evaluation:
# Determines whether the accuracy of the generated maps shall be evaluated
enabled: true
# The raw data will be recorded during the a simulation. After clicking the
# button Plot Slam Evaluation, raw data will be stored in
# the file \scripts as two csv files, while can be analysed through jupyter notebook.
enabled: false
# Determines the interval of when the accuracy of the generated maps is calculated
# A low interval (for example 1) causes performance problems
interval: 18
......@@ -244,10 +249,10 @@ slam:
# if true, data wil be associated by landmark identifiers, otherwise by minimum distance
associate_id: true
# Configures the 2D grid map of the occupancy grid mapping algorithm
# Configures the 2D occupancy grid mapping algorithm
mapping:
# Determines whether the mapping algorithm shall be executed
enabled: true
enabled: false
# Width of the map in meters
gridmap:
# Width of the map in meters
......
......@@ -92,20 +92,24 @@ viewer:
map:
# Configures the generated obstacles
obstacle:
# Configures octagon obstacles
octagon:
# Determines whether octagon obstacles shall be generated
# Configures features of maps
feature:
# Determines whether features of the maps shall be generated
enabled: false
# Radius of obstacles
# Radius of feature points
radius: 0.04
# Density of features
density: 0.15
# The following attributes are used if rectangular obstacles are disabled
# Minimum amount of generated obstacles
min_count: 25
min_count: 80
# Maximum amount of generated obstacles
max_count: 50
max_count: 150
# Minimum distance to origin
min_distance: 0.2
# Maximum distance to origin
max_distance: 2
max_distance: 2.5
# Configures rectangle obstacles
# Configures rectangle obstacles
rectangle:
# Determines whether rectangle obstacles shall be generated
......@@ -147,7 +151,6 @@ control:
# Criterion for stopping the following of a wall
progress_epsilon: 0.05
# Configures the SLAM system
slam:
# The amount of variables that describe the robot's state
# These are x position, y position and current angle theta
......@@ -157,12 +160,10 @@ slam:
# These are x position and y position
# Currently only supports 2
landmark_state_size: 2
# Configures the sensor noise. The values are currently empirically chosen.
sensor_noise:
# Standard deviation of the detected distance in meters
detected_distance: 0.2
# Standard deviation of the detected angle in degrees
detected_angle: 30
# Determines whether landmark-identifiers is used
# It determines whether correspondences of landmarks are given, i.e. identifies of landmarks are given
feature_detector: true
ekf_slam:
# Determines whether the EKF SLAM algorithm shall be executed
enabled: false
......@@ -171,72 +172,96 @@ slam:
# Configures the motion noise. The values are currently empirically chosen.
motion_noise:
# Standard deviation of the robots x-coordinate in meters after executing a motion command.
x: 0.005
x: 0.0002
# Standard deviation of the robots y-coordinate in meters after executing a motion command.
y: 0.005
y: 0.0002
# Standard deviation of the robots angle in degrees after executing a motion command.
theta: 1
theta: 0.1
# Configures the sensor noise. The values are currently empirically chosen.
sensor_noise:
# Standard deviation of the detected distance in meters 0.2 0.01
detected_distance: 0.2
# Standard deviation of the detected angle in degrees
detected_angle: 30
fast_slam:
# Determines whether the FastSLAM algorithm shall be executed
enabled: false
# The mahalanobis distance threshold used in data association
distance_threshold: 0.15
distance_threshold: 0.125
# The number of used particles
n_particles: 100
n_particles: 150 # 80
# Configures the motion noise. The values are currently empirically chosen.
motion_noise:
# Standard deviation of the motion command's translational velocity in m/s.
translational_velocity: 0.005
translational_velocity: 0.01
# Standard deviation of the motion command's rotational velocity in rad/s.
rotational_velocity: 0.005
rotational_velocity: 0.0001
# Configures the sensor noise. The values are currently empirically chosen.
sensor_noise:
# Standard deviation of the detected distance in meters 0.2 0.01
detected_distance: 0.2
# Standard deviation of the detected angle in degrees
detected_angle: 30
graph_based_slam:
# Determines whether the Graph-based SLAM algorithm shall be executed
enabled: false
# The euclidean distance threshold used in data association
distance_threshold: 0.2
# The number interval of pose-vertices added that the graph optimization is executed.
optimization_interval: 100
# The euclidean distance threshold [m] used in data association.
# If the distance between the estimated landmarks via slam and via actual measurement
# larger than the threshold, start backend
distance_threshold: 0.1
# The timestep interval of executing the frontend part.
frontend_interval: 5
# Pose density of frontend, meaning the minimum distance [m] between the current pose and the last pose.
frontend_pose_density: 0.1
# number of fixed vertexes while the graph optimization
num_fixed_vertexes: 20
# draw trajectory on the frame
draw_trajectory: false
# sparse solver: cholesky or spsolve.
# For the faster sparse solver cholesky, users have to install the scikit-sparse library.
solver: "cholesky"
# Configures the motion noise. The values are currently empirically chosen.
motion_noise:
# Standard deviation of the robots x-coordinate in meters after executing a motion command.
x: 0.005
x: 0.01
# Standard deviation of the robots y-coordinate in meters after executing a motion command.
y: 0.005
# Standard deviation of the robots angle in degrees after executing a motion command.
theta: 1.0 #0.09
y: 0.01
# Standard deviation of the robots angle in degrees after executing a motion command in degrees.
theta: 1.0
# Configures the sensor noise. The values are currently empirically chosen.
sensor_noise:
x: 0.2
y: 0.2
x: 0.02
y: 0.02
# Configures the evaluation of the SLAM algorithms
evaluation:
# Determines whether the accuracy of the generated maps shall be evaluated
# The raw data will be recorded during the a simulation. After clicking the
# button Plot Slam Evaluation, raw data will be stored in
# the file \scripts as two csv files, while can be analysed through jupyter notebook.
enabled: false
# Determines the interval of when the accuracy of the generated maps is calculated
# A low interval (for example 1) causes performance problems
interval: 20
interval: 18
# Determine the method of data association,
# if true, landmark identifiers will be associated, otherwise by minimum distance
associate_id: true
# Configures the 2D grid map of the occupancy grid mapping algorithm
# Configures the 2D occupancy grid mapping algorithm
mapping:
# Determines whether the mapping algorithm shall be executed
enabled: false
# Width of the map in meters
gridmap:
# Width of the map in meters
width: 5
width: 8
# Height of the map in meters
height: 5
# Number of pixels per meter. The GUI has problem while using grid map of high resolution.
resolution: 10
# offset the origin in the grid map
offset:
# offset in meters in horizontal direction
x: 2.5
# offset in meters in vertical direction
y: 2.5
height: 8
# Number of grids per meter. Note that high resolution will lead to performance issues.
resolution: 20
path_planning:
enabled: false
# Determines whether the path planning algorithm shall be executed
enabled: true
# Determines how importance the heuristic term is.
heuristic_weight: 1.0
\ No newline at end of file
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