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Commit 3fc4e072 authored by Lucas Pina's avatar Lucas Pina :skull_crossbones:
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Upload fortis_unity_ws setup

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.gitignore 0 → 100644
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.catkin_tools
.vscode
build
logs
devel
src/*
README.md
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# fortis_unity_ws
Development workspace for fortis project with unity sample
\ No newline at end of file
Development workspace for fortis project with unity sample
# Requirements:
## install:
#### install GTSAM lib for LIO-SAM
`sudo apt-get install -y software-properties-common`
`sudo add-apt-repository ppa:borglab/gtsam-release-4.0`
```bash
sudo apt install -y \
libgtsam-dev \
libgtsam-unstable-dev
```
#### install Move Base Flex dependency
`sudo apt install ros-noetic-costmap-2d`
`sudo apt install ros-noetic-nav-core`
`sudo apt install ros-noetic-move-base-flex`
#### install Open3D
# Execute:
`./tmux/execute_sim.sh`
#### PIP install
tsp_solver2
frames.gv 0 → 100644
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digraph G {
"base_link" -> "manitou_imu"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 8.163 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"map" -> "base_link"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "top_left_lidar"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "top_rigth_lidar"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "top_center_lidar"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "camera_frame"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "wheel_BL"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "wheel_BR"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "wheel_FR"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "wheel_FL"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"map" -> "odom"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
edge [style=invis];
subgraph cluster_legend { style=bold; color=black; label ="view_frames Result";
"Recorded at time: 109.800"[ shape=plaintext ] ;
}->"map";
}
\ No newline at end of file
ros_config/.gitkeep 0 → 100644
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ros_config/dm_run_behavior/fortis_fsm.yaml 0 → 100644
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behavior_name: fortis_fsm
args:
# Init wait time
waiting_time: 1
ros_config/dm_run_behavior/fortis_load_pallet.yaml 0 → 100644
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behavior_name: fortis_load_pallet
args:
# Init wait time
waiting_time: 2
n_iterations: 4
base_frame: "base_link"
offset_front_pallet: 2.5
ros_config/dm_run_behavior/fortis_nav.yaml 0 → 100644
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behavior_name: fortis_nav
args:
# Init wait time
waiting_time: 2
# # Waypoint navigation action node
# waypoint_navigation_topic: waypoint_navigation
# waypoint_controller: ""
# waypoint_planner: ""
# waypoint_recovery_behaviors: ""
waypoint_start_index: 10
# # Database access
# db_dsn: postgresql://root@127.0.0.1:26257/nakama?sslmode=disable
# db_storage: storage
# # Use geographic coordinates as waypoints?
ll_coords_data: 0
# # (ll_coords_data == false)
# db_path_collection: Paths
# # (ll_coords_data == true)
# db_ll_coord_collection: GeoCoordinates
# ll_conv_srv_topic: fromLL
# ll_coords_map_frame: map
# # set datum before executing?
# # This is used to make sure that the dispatched coordinates are relative to the correct
# # geographic reference (saved during the recording of cartesian coordinates).
# set_datum: 0
# set_datum_srv_topic: set_datum
inputs:
waypoints_id: wpn_001 #""
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ros_config/dm_run_behavior/fortis_task.yaml 0 → 100644
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behavior_name: fortis_task
args:
# Init wait time
waiting_time: 10
ros_config/dm_run_behavior/fortis_unload_pallet.yaml 0 → 100644
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behavior_name: fortis_unload_pallet
args:
base_frame: "base_link"
map_frame: "map"
ros_config/dm_run_behavior/record_cartesian_coords.yaml 0 → 100644
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behavior_name: record_cartesian_coords
args:
# Capture info
coord_topic: odom
num_of_coords: 4
min_allowed_trust: 0.0
tf_poses: 1
tf_poses_frame: map
# Database access
dsn: postgresql://root@127.0.0.1:26257/nakama?sslmode=disable
storage: storage
path_collection: Paths
create_if_empty: 1
overwrite_if_exists: 1
inputs:
path_id: wpn_001 #""
ros_config/dm_run_behavior/record_discrete_cartesian_coords.yaml 0 → 100644
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behavior_name: record_discrete_cartesian_coords
args:
# Capture info
coord_topic: /waypoints #/odom #/lio_sam/mapping/odometry #ekf/local_odom #ekf/global_odom
num_of_coords: 40
min_allowed_trust: 0.0
tf_poses: 1
tf_poses_frame: map
# Database access
dsn: postgresql://root@127.0.0.1:26257/nakama?sslmode=disable
storage: storage
path_collection: Paths
create_if_empty: 1
overwrite_if_exists: 1
inputs:
path_id: wpn_001 #""
ros_config/dm_run_behavior/record_geographic_coords.yaml 0 → 100644
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behavior_name: record_geographic_coords
args:
# Capture info
navsatfix_topic: /gps/fix
num_of_coords: 5 #1
min_allowed_trust: 0.9
# Database access
dsn: postgresql://root@127.0.0.1:26257/nakama?sslmode=disable
storage: storage
ll_coords_collection: GeoCoordinates
create_if_empty: 1
overwrite_if_exists: 0
inputs:
ll_coords_id: rrt_exploration_vertices #""
ros_config/dm_run_behavior/waypoint_navigation_procedure.yaml 0 → 100644
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behavior_name: waypoint_navigation_procedure
args:
# Waypoint navigation action node
waypoint_navigation_topic: waypoint_navigation
waypoint_controller: ""
waypoint_planner: ""
waypoint_recovery_behaviors: ""
waypoint_start_index: 0
# Database access
db_dsn: postgresql://root@127.0.0.1:26257/nakama?sslmode=disable
db_storage: storage
# Use geographic coordinates as waypoints?
ll_coords_data: 0
# (ll_coords_data == false)
db_path_collection: Paths
# (ll_coords_data == true)
db_ll_coord_collection: GeoCoordinates
ll_conv_srv_topic: fromLL
ll_coords_map_frame: map
# set datum before executing?
# This is used to make sure that the dispatched coordinates are relative to the correct
# geographic reference (saved during the recording of cartesian coordinates).
set_datum: 0
set_datum_srv_topic: set_datum
inputs:
waypoints_id: wpn_001 #""
ros_config/loc_lio_sam.yaml 0 → 100644
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lio_sam:
# Topics
pointCloudTopic: "front_lslidar_point_cloud_filtered" # "back_lslidar_point_cloud_filtered" # "front_lslidar_point_cloud_filtered" "fused_point_cloud" # Point cloud data
imuTopic: "imu/wit/imu" # IMU data
odomTopic: "odom" # IMU pre-preintegration odometry, same frequency as IMU
gpsTopic: "odometry/gps" # GPS odometry topic from navsat, see module_navsat.launch file
# Topics - Ingeniarius' version at: https://gitlab.ingeniarius.pt/ingeniarius_common/perception/lio-sam.git
gpsFixTopic: "/loader/ugv1/gps/fix" # GPS navsatfix topic from sensor, used to determine wether to apply GPS factor or not
gpsFixFilteredTopic: "" # Its subscribed but not used for anything yet
# Frames
lidarFrame: "base_link"
baselinkFrame: "base_link"
odometryFrame: "odom"
mapFrame: "map"
# Frames - Ingeniarius
mapFrameAsChild: false # Pass the map_frame as child of base_link, following the CTU MRS approach (https://ctu-mrs.github.io/docs/system/frames_of_reference.html)
# Ingeniarius' version at: https://gitlab.ingeniarius.pt/ingeniarius_common/perception/lio-sam.git
liosamMode: 0 # LIO-SAM Mode 0: SLAM, Mode 1: Localization, Mode 2: Application
gpsInitialLocalization: true
gpsUpatedDistFix: 2.0 # Min update distance for liosam update is mapframe went it have GPSfix, status = 2
# GPS Settings
useImuHeadingInitialization: true # if using GPS data, set to "true"
useGpsElevation: true # if GPS elevation is bad, set to "false"
gpsCovThreshold: 2.0 # m^2, threshold for using GPS data
poseCovThreshold: 25.0 # m^2, threshold for using GPS data
# Export settings
savePCD: false # https://github.com/TixiaoShan/LIO-SAM/issues/3
savePCDDirectory: "/scliosam/data/" # use global path, and end with "/". Warning: if you have already data in the above savePCDDirectory, it will all remove and remake them. Thus, backup is recommended if pre-made data exist.
# Ingeniarius' version at: https://gitlab.ingeniarius.pt/ingeniarius_common/perception/lio-sam.git
eraseLOAMFolder: false
saveRawCloud: false
graphTest: false # Save factor graph drawing
saveGTSAM: false # Save graph
saveGTSAMDirectory: "gtsam/" # Directory of gtsam
# Analysis Localization
buildConfusionMatrix: false # Build a confusion matrix
savePCDDirectory_Q: "/scliosam/" # Directory to save the matrix
# Quality of localization
covarianceMedium: 1.0
covarianceHigh: 2.0
# Sensor Settings
sensor: velodyne # lidar sensor type, 'velodyne' or 'ouster' or 'livox' or 'leishen'
N_SCAN: 16 # number of lidar channel (i.e., 16, 32, 64, 128)
Horizon_SCAN: 2000 # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048)
downsampleRate: 1 # default: 1. Downsample your data if too many points. i.e., 16 = 64 / 4, 16 = 16 / 1
lidarMinRange: 1.0 # default: 1.0, minimum lidar range to be used
lidarMaxRange: 1000.0 # default: 1000.0, maximum lidar range to be used
# IMU Settings
imuAccNoise: 3.9939570888238808e-03
imuGyrNoise: 1.5636343949698187e-03
imuAccBiasN: 6.4356659353532566e-05
imuGyrBiasN: 3.5640318696367613e-05
imuGravity: 9.80511
imuRPYWeight: 0.01
# Extrinsics (lidar -> IMU)
extrinsicTrans: [0.0, 0.0, 0.0]
extrinsicRot: [1, 0, 0, 0, 1, 0, 0, 0, 1]
extrinsicRPY: [1, 0, 0, 0, 1, 0, 0, 0, 1]
# LOAM feature threshold
edgeThreshold: 1.0
surfThreshold: 0.1
edgeFeatureMinValidNum: 10
surfFeatureMinValidNum: 100
# voxel filter paprams
odometrySurfLeafSize: 0.2 # default: 0.4 - outdoor, 0.2 - indoor
mappingCornerLeafSize: 0.1 # default: 0.2 - outdoor, 0.1 - indoor
mappingSurfLeafSize: 0.2 # default: 0.4 - outdoor, 0.2 - indoor
# robot motion constraint (in case you are using a 2D robot)
z_tollerance: 1000 # meters
rotation_tollerance: 1000 # radians
# CPU Params
numberOfCores: 4 # number of cores for mapping optimization
mappingProcessInterval: 0.15 # seconds, regulate mapping frequency
# Surrounding map
surroundingkeyframeAddingDistThreshold: 1.0 # meters, regulate keyframe adding threshold
surroundingkeyframeAddingAngleThreshold: 0.2 # radians, regulate keyframe adding threshold
surroundingKeyframeDensity: 2.0 # meters, downsample surrounding keyframe poses
surroundingKeyframeSearchRadius: 50.0 # meters, within n meters scan-to-map optimization (when loop closure disabled)
# Loop closure
loopClosureEnableFlag: true
loopClosureFrequency: 1.0 # Hz, regulate loop closure constraint add frequency
surroundingKeyframeSize: 50 # submap size (when loop closure enabled)
historyKeyframeSearchRadius: 15.0 # meters, key frame that is within n meters from current pose will be considerd for loop closure
historyKeyframeSearchTimeDiff: 30.0 # seconds, key frame that is n seconds older will be considered for loop closure
historyKeyframeSearchNum: 25 # number of hostory key frames will be fused into a submap for loop closure
historyKeyframeFitnessScore: 0.3 # icp threshold, the smaller the better alignment
# Ingeniarius' version at: https://gitlab.ingeniarius.pt/ingeniarius_common/perception/lio-sam.git
performRSLoopClosureFlag: true # Perform RS Loop Closure
performSCLoopClosureFlag: false # Perform SC Loop Closure
# Visualization
globalMapVisualizationSearchRadius: 1000.0 # meters, global map visualization radius
globalMapVisualizationPoseDensity: 1.0 #10.0 # meters, global map visualization keyframe density
globalMapVisualizationLeafSize: 0.4 #0.20 # meters, global map visualization cloud density
# Ingeniarius' version at: https://gitlab.ingeniarius.pt/ingeniarius_common/perception/lio-sam.git
# ICP Localization
icpLocalizationStep: 1 # Step
icpLocalizationAscendingOrder: false # Ascending order
icpLocalizationPreliminary: false # ICP Preliminary Localization
# PCD Localization
pcdInitialLocalization: false # Initial localization using PCD
pcdLocalizationThreshold: 0.0
# SCD Localization
scdInitialLocalization: true # Initial localization using SCD
scdPCNumRing: 20
scdPCNumSector: 60
scdPCMaxRadius: 80.0
scdInput: single_scan # single_scan single_feature multi_scan
numberGpsOdometryMessageToInitialize: 30 # minimum number of navsat's odometry messages in order to initialize the GPS factor
# Relocalization
relocalizeMode: 3 # 0 - Rviz, 1 - trajectory, 2 - searching
relocalizeThreadFrequency: 1.0 # the frequency of the relocalization thread logic
relocalizeGlobalTimeout: 10.0 # global relocalization timeout. The period at which a better localization for the odom frame is looked.
relocalizeHistoryKeyframeFitnessScore: 1.0 # the fitness score threshold for relocalization.
setMaxCorrespondenceDistance:
100 # Set the maximum distance threshold between two correspondent points in source <-> target
# If the distance is larger than this threshold, the points will be ignored in the alignment process
setMaximumIterations: 100 # Set the maximum number of iterations the internal optimization should run for
setTransformationEpsilon: 0.000001 # The maximum difference between two consecutive transformations in order to consider convergence
setEuclideanFitnessEpsilon:
0.000001 # Set the maximum allowed Euclidean error between two consecutive steps in the ICP loop, before the
# algorithm is considered to have converged. The error is estimated as the sum of the differences
# between correspondences in an Euclidean sense, divided by the number of correspondences;
setRANSACIterations: 0 # Set the number of iterations RANSAC should run for.
numberOfAttempts: 10
useICPscanMatchGlobal: false
initMapGPS: false
numFramesEKF: 10
# Navsat (convert GPS coordinates to Cartesian)
navsat:
frequency: 50
wait_for_datum: true
delay: 0.0
magnetic_declination_radians: 0
yaw_offset: 0
zero_altitude: true
altitude: 0.0
broadcast_utm_transform: false
broadcast_utm_transform_as_parent_frame: false
publish_filtered_gps: true
# EKF for Navsat
ekf_gps:
publish_tf: false
map_frame: map
odom_frame: odom
base_link_frame: base_link
world_frame: map
frequency: 50
two_d_mode: false
sensor_timeout: 0.01
# -------------------------------------
# External IMU:
# -------------------------------------
imu0: "imu/wit/imu"
# make sure the input is aligned with ROS REP105. "imu_correct" is manually transformed by myself. EKF can also transform the data using tf between your imu and base_link
imu0_config:
[
false,
false,
false,
true,
true,
true,
false,
false,
false,
false,
false,
true,
true,
true,
true,
]
imu0_differential: false
imu0_queue_size: 50
imu0_remove_gravitational_acceleration: true
# -------------------------------------
# Odometry (From Navsat):
# -------------------------------------
odom0: odometry/gps
odom0_config:
[
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
]
odom0_differential: false
odom0_queue_size: 10
# x y z r p y x_dot y_dot z_dot r_dot p_dot y_dot x_ddot y_ddot z_ddot
process_noise_covariance:
[
1.0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1.0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
10.0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.03,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.03,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.25,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.25,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.04,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.01,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.01,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.5,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.01,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.01,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.015,
]
# initial_estimate_covariance: [1e-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 1e-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 1e-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 1e-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 1e-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 1e-1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 1e-1, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 1e-1, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 1e-1, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-1, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-1, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-1, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-1, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-1, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-1]
ros_config/nav_move_base_flex/common_costmap_params.yaml 0 → 100644
View file @ 3fc4e072
# footprint: [[0.94, -0.84], [0.94, 0.84], [-1.66, 0.84], [-1.66, -0.84]] # without tool
# footprint: [[1.59, -0.84], [1.59, 0.84], [-1.66, 0.84], [-1.66, -0.84]] # with fork
footprint: [[2.7, -0.84], [2.7, 0.84], [-1.66, 0.84], [-1.66, -0.84]] # with tool 2.30
footprint_padding: 0.0
make_plan_clear_costmap: false # parameters of make_plan service
make_plan_add_unreachable_goal: false # true, parameters of make_plan service
global_static_layer:
enabled: true # Whether to apply this plugin or not
first_map_only: false # Only subscribe to the first message on the map topic, ignoring all subsequent messages
unknown_cost_value: -1 # The value for which a cost should be considered unknown when reading in a map from the map server. If the costmap is not tracking unknown space, costs of this value will be considered occupied. A value of zero also results in this parameter being unused.
lethal_cost_threshold: 100 # The threshold value at which to consider a cost lethal when reading in a map from the map server.
subscribe_to_updates: true # In addition to map_topic, also subscribe to map_topic + "_updates"
track_unknown_space: true # If true, unknown values in the map messages are translated directly to the layer. Otherwise, unknown values in the map message are translated as FREE_SPACE in the layer.
use_maximum: false # Only matters if the static layer is not the bottom layer. If true, only the maximum value will be written to the master costmap
trinary_costmap: true # If true, translates all map message values to NO_INFORMATION/FREE_SPACE/LETHAL_OBSTACLE (three values). If false, a full spectrum of intermediate values is possible.
map_topic: /loader/ugv1/lio_sam/mapping/map_global/map #The topic that the costmap subscribes to for the static map. This parameter is useful when you have multiple costmap instances within a single node that you want to use different static maps.
global_inflation_layer:
enabled: true # Whether to apply this plugin or not
cost_scaling_factor: 3.0 # exponential rate at which the obstacle cost drops off (default: 10)
inflation_radius: 3.0 # 1.0m out of 1.6m of robot's width (the robot is actually wider)
global_obstacle_layer_0:
enabled: true
voxel_decay: 10 # seconds if linear, e^n if exponential
decay_model: 0 # 0=linear, 1=exponential, -1=persistent
voxel_size: 0.05 # meters
track_unknown_space: true # default space is known
mark_threshold: 0 # voxel height
update_footprint_enabled: true
combination_method: 1 # 1=max, 0=override
origin_z: 0.0 # meters
publish_voxel_map: true # default off
transform_tolerance: 0.6 # seconds
mapping_mode: false # default off, saves map not for navigation
map_save_duration: 60 # default 60s, how often to autosave
observation_sources: pc1_mark pc1_clear
pc1_mark:
data_type: PointCloud2
topic: /loader/ugv1/lio_sam/deskew/cloud_deskewed
marking: true
clearing: false
obstacle_range: 3.0 # meters
min_obstacle_height: 0.3 # default 0, meters
max_obstacle_height: 2.0 # default 3, meters
expected_update_rate: 0.0 # default 0, if not updating at this rate at least, remove from buffer
observation_persistence: 0.0 # default 0, use all measurements taken during now-value, 0=latest
inf_is_valid: false # default false, for laser scans
filter: "passthrough" # default passthrough, apply "voxel", "passthrough", or no filter to sensor data, recommend on
voxel_min_points: 0 # default 0, minimum points per voxel for voxel filter
clear_after_reading: true # default false, clear the buffer after the layer gets readings from it
pc1_clear:
data_type: PointCloud2
topic: /loader/ugv1/lio_sam/deskew/cloud_deskewed
marking: false
clearing: true
max_z: 7.0 # default 0, meters
min_z: 0.1 # default 10, meters (the back lidar has 4m blind spot)
vertical_fov_angle: 0.4 #523 # default 0.7, radians. For 3D lidars it's the symmetric FOV about the planar axis.
vertical_fov_padding: 0.05 # 3D Lidar only. Default 0, in meters
horizontal_fov_angle: 6.29 # 3D lidar scanners like the VLP16 have 360 deg horizontal FOV.
decay_acceleration: 1.0 # default 0, 1/s^2.
model_type: 1 # default 0, model type for frustum. 0=depth camera, 1=3d lidar like VLP16 or similar
global_obstacle_layer_1:
enabled: false
voxel_decay: 60 # seconds if linear, e^n if exponential
decay_model: 0 # 0=linear, 1=exponential, -1=persistent
voxel_size: 0.2 # meters
track_unknown_space: true # default space is known
max_obstacle_height: 2.5 # meters
unknown_threshold: 15 # voxel height
mark_threshold: 3 # voxel height
update_footprint_enabled: true
combination_method: 0 # 1=max, 0=override
obstacle_range: 5.0 # meters
origin_z: 0.0 # meters
publish_voxel_map: false # default off
transform_tolerance: 0.6 # seconds
mapping_mode: false # default off, saves map not for navigation
map_save_duration: 60 # default 60s, how often to autosave
observation_sources: pc2_mark pc2_clear
pc2_mark:
data_type: PointCloud2
topic: /traversability_map_visualization/traversability_cloud/passed
marking: true
clearing: false
min_obstacle_height: 0.0 # default 0, meters (initial: 0.05)
max_obstacle_height: 2.5 # default 3, meters
expected_update_rate: 0.0 # default 0, if not updating at this rate at least, remove from buffer
observation_persistence: 0.0 # default 0, use all measurements taken during now-value, 0=latest
inf_is_valid: false # default false, for laser scans
filter: "passthrough" # default passthrough, apply "voxel", "passthrough", or no filter to sensor data, recommend on
voxel_min_points: 0 # default 0, minimum points per voxel for voxel filter
clear_after_reading: true # default false, clear the buffer after the layer gets readings from it
pc2_clear:
enabled: true #default true, can be toggled on/off with associated service call
data_type: PointCloud2
topic: /traversability_map_visualization/traversability_cloud/passed
marking: false
clearing: true
max_z: 5.0 # default 0, meters
min_z: 0.0 # default 10, meters (the back lidar has 4m blind spot)
vertical_fov_angle: 0.523 # default 0.7, radians. For 3D lidars it's the symmetric FOV about the planar axis.
vertical_fov_padding: 0.05 # 3D Lidar only. Default 0, in meters
horizontal_fov_angle: 6.29 # 3D lidar scanners like the VLP16 have 360 deg horizontal FOV.
decay_acceleration: 5.0 # default 0, 1/s^2.
model_type: 1 # default 0, model type for frustum. 0=depth camera, 1=3d lidar like VLP16 or similar
local_static_layer_0:
enabled: true
first_map_only: false
unknown_cost_value: -1
lethal_cost_threshold: 100
subscribe_to_updates: true
track_unknown_space: true
use_maximum: false
trinary_costmap: true
map_topic: /loader/ugv1/lio_sam/mapping/map_global/map
local_static_layer_1:
enabled: true
first_map_only: false
unknown_cost_value: -1
lethal_cost_threshold: 100
subscribe_to_updates: false
track_unknown_space: true
use_maximum: false
trinary_costmap: true
map_topic: /traversability_map_visualization/traversability_map
local_inflation_layer:
enabled: true # Whether to apply this plugin or not
cost_scaling_factor: 10 # exponential rate at which the obstacle cost drops off (default: 10)
inflation_radius: 1.0 # max. distance from an obstacle at which costs are incurred for planning paths (min » costmap resolution)
local_obstacle_layer_0:
enabled: true
voxel_decay: 2 # seconds if linear, e^n if exponential
decay_model: 0 # 0=linear, 1=exponential, -1=persistent
voxel_size: 0.1 # meters
track_unknown_space: true # default space is known
max_obstacle_height: 5.0 # meters
unknown_threshold: 15 # voxel height
mark_threshold: 3 # voxel height
update_footprint_enabled: true
combination_method: 1 # 1=max, 0=override
obstacle_range: 10.0 # meters
origin_z: 0.0 # meters
publish_voxel_map: false # default off
transform_tolerance: 0.6 # seconds
mapping_mode: false # default off, saves map not for navigation
map_save_duration: 60 # default 60s, how often to autosave
observation_sources: pc1_mark pc1_clear
pc1_mark:
data_type: PointCloud2
topic: /loader/ugv1/lio_sam/deskew/cloud_deskewed
marking: true
clearing: false
min_obstacle_height: 0.2 # default 0, meters
max_obstacle_height: 2.0 # default 3, meters
expected_update_rate: 0.0 # default 0, if not updating at this rate at least, remove from buffer
observation_persistence: 0.0 # default 0, use all measurements taken during now-value, 0=latest
inf_is_valid: false # default false, for laser scans
filter: "passthrough" # default passthrough, apply "voxel", "passthrough", or no filter to sensor data, recommend on
voxel_min_points: 0 # default 0, minimum points per voxel for voxel filter
clear_after_reading: true # default false, clear the buffer after the layer gets readings from it
pc1_clear:
enabled: true #default true, can be toggled on/off with associated service call
data_type: PointCloud2
topic: /loader/ugv1/lio_sam/deskew/cloud_deskewed
marking: false
clearing: true
max_z: 10.0 # default 0, meters
min_z: 0.0 # default 10, meters (the back lidar has 4m blind spot)
vertical_fov_angle: 0.523 # default 0.7, radians. For 3D lidars it's the symmetric FOV about the planar axis.
vertical_fov_padding: 0.05 # 3D Lidar only. Default 0, in meters
horizontal_fov_angle: 6.29 # 3D lidar scanners like the VLP16 have 360 deg horizontal FOV.
decay_acceleration: 5.0 # default 0, 1/s^2.
model_type: 1 # default 0, model type for frustum. 0=depth camera, 1=3d lidar like VLP16 or similar
local_obstacle_layer_1:
enabled: false
voxel_decay: 15 # seconds if linear, e^n if exponential
decay_model: 0 # 0=linear, 1=exponential, -1=persistent
voxel_size: 0.2 # meters
track_unknown_space: true # default space is known
max_obstacle_height: 2.5 # meters
unknown_threshold: 15 # voxel height
mark_threshold: 3 # voxel height
update_footprint_enabled: true
combination_method: 0 # 1=max, 0=override
obstacle_range: 5.0 # meters
origin_z: 0.0 # meters
publish_voxel_map: false # default off
transform_tolerance: 0.6 # seconds
mapping_mode: false # default off, saves map not for navigation
map_save_duration: 60 # default 60s, how often to autosave
observation_sources: pc2_mark pc2_clear
pc2_mark:
data_type: PointCloud2
topic: /traversability_map_visualization/traversability_cloud/passed
marking: true
clearing: false
min_obstacle_height: 0.0 # default 0, meters (initial: 0.05)
max_obstacle_height: 2.5 # default 3, meters
expected_update_rate: 0.0 # default 0, if not updating at this rate at least, remove from buffer
observation_persistence: 0.0 # default 0, use all measurements taken during now-value, 0=latest
inf_is_valid: false # default false, for laser scans
filter: "passthrough" # default passthrough, apply "voxel", "passthrough", or no filter to sensor data, recommend on
voxel_min_points: 0 # default 0, minimum points per voxel for voxel filter
clear_after_reading: true # default false, clear the buffer after the layer gets readings from it
pc2_clear:
enabled: true #default true, can be toggled on/off with associated service call
data_type: PointCloud2
topic: /traversability_map_visualization/traversability_cloud/passed
marking: false
clearing: true
max_z: 5.0 # default 0, meters
min_z: 0.0 # default 10, meters (the back lidar has 4m blind spot)
vertical_fov_angle: 0.523 # default 0.7, radians. For 3D lidars it's the symmetric FOV about the planar axis.
vertical_fov_padding: 0.05 # 3D Lidar only. Default 0, in meters
horizontal_fov_angle: 6.29 # 3D lidar scanners like the VLP16 have 360 deg horizontal FOV.
decay_acceleration: 5.0 # default 0, 1/s^2.
model_type: 1 # default 0, model type for frustum. 0=depth camera, 1=3d lidar like VLP16 or similar
ros_config/nav_move_base_flex/controllers.yaml 0 → 100644
View file @ 3fc4e072
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ros_config/nav_move_base_flex/global_costmap_params.yaml 0 → 100644
View file @ 3fc4e072
global_costmap:
global_frame: map
robot_frame: base_link
robot_base_frame: base_link
transform_tolerance: 1.0
update_frequency: 2.0
publish_frequency: 2.0
rolling_window: false
width: 30.0
height: 30.0
resolution: 0.2
origin_x: 0.0
origin_y: 0.0
always_send_full_costmap: true
plugins:
- { name: global_static_layer, type: "costmap_2d::StaticLayer" }
- { name: global_inflation_layer, type: "costmap_2d::InflationLayer" }
# - { name: global_obstacle_layer_0, type: "spatio_temporal_voxel_layer/SpatioTemporalVoxelLayer" }
ros_config/nav_move_base_flex/local_costmap_params.yaml 0 → 100644
View file @ 3fc4e072
1local_costmap:
global_frame: map
robot_frame: base_link
robot_base_frame: base_link
transform_tolerance: 1.0
update_frequency: 2.0
publish_frequency: 2.0
rolling_window: true
width: 30.0
height: 30.0
resolution: 0.2
origin_x: 0.0
origin_y: 0.0
always_send_full_costmap: true
plugins:
- { name: local_static_layer_0, type: "costmap_2d::StaticLayer" }
- { name: local_inflation_layer, type: "costmap_2d::InflationLayer" }
# - { name: local_obstacle_layer_0, type: "spatio_temporal_voxel_layer/SpatioTemporalVoxelLayer" }
ros_config/nav_move_base_flex/mbf_costmap_nav.yaml 0 → 100644
View file @ 3fc4e072
#########################
### Static Parameters ###
#########################
robot_frame: base_link # the frame of the robot, which will be used to determine its position
map_frame: map # the global frame the robot is controlling in
global_frame: map
# force_stop_at_goal: false # force move base flex to stop the robot once the goal is reached
# force_stop_on_cancel: false # force move base flex to stop the robot on navigation cancellation
# mbf_tolerance_check: false # force move base flex to check for the goal tolerance
# dist_tolerance: 0.1 # distance tolerance to the given goal pose
# angle_tolerance: 0.175 # angle tolerance to the given goal pose
# tf_timeout: 1.0 # time before a timeout used for tf requests
#################################################
### Dynamically Reconfigurable MBF Parameters ###
#################################################
# Planners
planners: # global planner, e.g. navfn/NavfnROS
- name: GlobalPlanner
type: global_planner/GlobalPlanner
planner_frequency: $(arg planner_frequency) # the rate in Hz at which to run the planning loop
planner_max_retries: $(arg planner_max_retries) # how many times we will recall the planner in an attempt to find a valid plan before giving up
planner_patience: $(arg planner_patience) # how long the planner will wait in seconds in an attempt to find a valid plan before giving up
# Controllers
controllers: # list of controller, e.g. eband_local_planner/EBandPlannerROS
- name: TebLocalPlannerROS
type: teb_local_planner/TebLocalPlannerROS
# - name: GoToPlanner
# type: teb_local_planner/TebLocalPlannerROS
# - name: UnloadPallet
# type: teb_local_planner/TebLocalPlannerROS
controller_frequency: $(arg controller_frequency) # the rate in Hz at which to run the control loop and send velocity commands to the base
controller_max_retries: $(arg controller_max_retries) # how many times we will recall the controller in an attempt to find a valid command before giving up
controller_patience: $(arg controller_patience) # how long the controller will wait in seconds without receiving a valid control before giving up
# Oscillation
oscillation_timeout: 0.0 # how far in meters the robot must move to be considered not to be oscillating
oscillation_distance: 0.5 # how long in seconds to allow for oscillation before executing recovery behaviors
# Other
recovery_enabled: $(arg recovery_enabled) # enable the move_base_flex recovery behaviors to attempt to clear out space (for some reason the modern name does not work)
recovery_patience: 15.0 # how much time we allow recovery behaviors to complete before canceling (or stopping if cancel fails).
restore_defaults: false # restore to the original configuration
shutdown_costmaps: false # shutdown the costmaps of the node when move_base_flex is in an inactive state.
shutdown_costmaps_delay: 1.0 # how long in seconds to wait after last action before shutting down the costmaps.
# the remaining parameters do not seem to exist here: http://wiki.ros.org/move_base_flex#Navigation_Servers
# <param name="switch_controller_keyword_name" value="Docking"/> <!--the recovery will search for controllers with this name inside-->
# NOTES:
# - parameter sources: https://uos.github.io/mbf_docs/tutorials/beginner/parameters/mbf_parameters/ and
# https://wiki.ros.org/move_base_flex#Navigation_Servers.
#
# - controller_frequency can be set to a lower value (than the 10.0 as default) to prevent
# an high cmd_vel rate, which caused the robot to some discontinuous movements.
#
# - planner_frequency can be set to 0.0 in order to make the global_planner to run only when
# a new goal is received(it helps the robot to ignore drift during execution).
#
# - legacy "max_planning_retries" = mbf "planner_max_retries".
#
# - oscillation_timeout set to 0.0 in order to avoid the robot to get stuck when some oscillations are present.
#
# - planner_max_retries, controller_max_retries and recovery_enabled, these parameters were never set explicitly by us
# (source: https://wiki.ros.org/move_base_flex#Navigation_Servers and https://uos.github.io/mbf_docs/tutorials/beginner/parameters/mbf_parameters/)
#
# - shutdown_costmaps and shutdown_costmaps_delay, never set explecitily set by us before. Allow to shutdown costmaps,
# when move base is in an inactive state (might be good for optimisation).
#
# - legacy "recovery_behavior_enabled" = mbf "recovery_enabled".
#
# - there were legacy move base parameters in previous configuration: "conservative_reset_dist" and "clearing_rotation_allowed".
# Check the relay for more information https://github.com/magazino/move_base_flex/blob/master/mbf_costmap_nav/scripts/move_base_legacy_relay.py.
#
ros_config/nav_move_base_flex/planners.yaml 0 → 100644
View file @ 3fc4e072
GlobalPlanner:
# Lethal Cost (dynamic reconfigure)
lethal_cost: 253 # --- default ---
# Neutral Cost (dynamic reconfigure)
neutral_cost: 50 # --- default ---
# Factor to multiply each cost from costmap by (dynamic reconfigure)
cost_factor: 3.0 # --- default ---
# Specifies whether or not to visualize the potential area computed via a PointCloud2.
visualize_potential: true # --- default ---
# Publish Potential Costmap (dynamic reconfigure)
publish_potential: true # --- default ---
# How to set the orientation of each point (dynamic reconfigure)
# None=0, Forward=1, Interpolate=2, ForwardThenInterpolate=3,
# Backward=4, Leftward=5, Rightward=6
orientation_mode: 0 # --- default ---
# What window to use to determine the orientation based on the
# position derivative specified by the orientation mode (dynamic reconfigure)
orientation_window_size: 1 # --- default ---
# If for some reason, you want global_planner to exactly
# mirror the behavior of navfn, set this to true
old_navfn_behavior: false # --- default ---
# If true, create a path that follows the grid boundaries.
# Otherwise, use a gradient descent method.
use_grid_path: false # --- default ---
# If true, use the quadratic approximation of the potential.
# Otherwise, use a simpler calculation.
use_quadratic: true
# If true, use dijkstra's algorithm.
# Otherwise, A*.
use_dijkstra: true # --- default ---
# A tolerance on the goal point for the planner.
# The planner will attempt to create a plan that is as close to
# the specified goal as possible but no further than default_tolerance away
default_tolerance: 0.0
# Specifies whether or not to allow the planner to create plans that traverse unknown space. NOTE: if you are using a layered costmap_2d costmap with a voxel or obstacle layer, you must also set the track_unknown_space param for that layer to be true, or it will convert all your unknown space to free space (which planner will then happily go right through).
allow_unknown: false # *** change ***
# Outlines the global costmap with lethal obstacles.
# For the usage of a non static (rolling window) global costmap
# this needs to be set to fals
outline_map: true # --- default ---
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