nav2 stvl层配置后没有效果
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环境是wsl fishbot gazebo模拟
参考官方文档https://docs.nav2.org/tutorials/docs/navigation2_with_stvl.html和git仓库里的配置文件，配置以后没有任何效果，无法避开低于雷达高度的障碍物，在local_costmap中也没有现实障碍物，有人成功配置过或有参考配置吗
尝试了多个配置方案，在global_costmap中也配置过local_costmap:
local_costmap:
ros__parameters:
update_frequency: 5.0
publish_frequency: 2.0
global_frame: odom
robot_base_frame: base_link
use_sim_time: True
rolling_window: true
width: 3
height: 3
resolution: 0.05
robot_radius: 0.12
# plugins: ["voxel_layer", "stvl_layer","inflation_layer"]
plugins: ["stvl_layer","inflation_layer"]
stvl_layer:
plugin: "spatio_temporal_voxel_layer/SpatioTemporalVoxelLayer"
enabled: true
voxel_decay: 15.0 # seconds if linear, e^n if exponential
decay_model: 0 # 0=linear, 1=exponential, -1=persistent
voxel_size: 0.02 # 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: false # default off
transform_tolerance: 0.5 # seconds
mapping_mode: false # default off, saves map not for navigation
map_save_duration: 60.0 # default 60s, how often to autosave
observation_sources: rgbd1_mark rgbd1_clear
rgbd1_mark:
data_type: PointCloud2
topic: /camera_sensor/points
marking: true
clearing: false
obstacle_range: 3.0 # meters
min_obstacle_height: 0.1 # 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
rgbd1_clear:
data_type: PointCloud2
topic: /camera_sensor/points
marking: false
clearing: true
max_z: 7.0 # default 0, meters
min_z: 0.1 # default 10, meters
vertical_fov_angle: 0.8745 # default 0.7, radians
horizontal_fov_angle: 1.048 # default 1.04, radians
decay_acceleration: 5.0 # default 0, 1/s^2. If laser scanner MUST be 0
model_type: 0
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
cost_scaling_factor: 3.0
inflation_radius: 0.55
# voxel_layer:
# plugin: "nav2_costmap_2d::VoxelLayer"
# enabled: True
# publish_voxel_map: True
# origin_z: 0.0
# z_resolution: 0.05
# z_voxels: 16
# max_obstacle_height: 2.0
# mark_threshold: 0
# observation_sources: scan
# scan:
# topic: /scan
# max_obstacle_height: 2.0
# clearing: True
# marking: True
# data_type: "LaserScan"
# raytrace_max_range: 3.0
# raytrace_min_range: 0.0
# obstacle_max_range: 2.5
# obstacle_min_range: 0.0
# static_layer:
# plugin: "nav2_costmap_2d::StaticLayer"
# map_subscribe_transient_local: True
always_send_full_costmap: True
... other parameters ...
local_costmap:
local_costmap:
ros__parameters:
update_frequency: 5.0
publish_frequency: 2.0
global_frame: odom
robot_base_frame: base_link # Ensure this matches your robot's TF
use_sim_time: True
rolling_window: true
width: 3
height: 3
resolution: 0.05
robot_radius: 0.12 # Or use footprint parameter
transform_tolerance: 0.5 # Increased tolerance can help with timing issues
# List both layers here, plus inflation
plugins: ["scan_voxel_layer", "inflation_layer"]
# Configuration for the standard VoxelLayer (using LaserScan)
scan_voxel_layer:
plugin: "nav2_costmap_2d::VoxelLayer"
enabled: True
publish_voxel_map: True # Publishes to /local_costmap/scan_voxel_layer/voxel_grid (namespaced)
origin_z: 0.0
z_resolution: 0.05
z_voxels: 16
max_obstacle_height: 2.0
mark_threshold: 0
combination_method: 1 # 1=max
observation_sources: scan # Source name for this layer
scan: # Configuration for the 'scan' source
topic: /scan
max_obstacle_height: 2.0
clearing: True
marking: True
data_type: "LaserScan"
raytrace_max_range: 3.0
obstacle_max_range: 2.5
# Configuration for the Spatio-Temporal Voxel Layer (using PointCloud2)
# stvl_layer:
# plugin: "spatio_temporal_voxel_layer/SpatioTemporalVoxelLayer"
# enabled: true
# publish_voxel_map: true # Publishes to /local_costmap/stvl_layer/voxel_map (namespaced)
# voxel_decay: 10.0 # Shorter decay for local map
# decay_model: 0 # 0=linear
# voxel_size: 0.05 # Match resolution if desired
# track_unknown_space: true
# mark_threshold: 0
# update_footprint_enabled: true
# combination_method: 1 # 1=max
# origin_z: 0.0
# transform_tolerance: 0.5 # Increased tolerance
# mapping_mode: false
# observation_sources: pointcloud # Source name for this layer
# pointcloud: # Configuration for the 'pointcloud' source
# data_type: PointCloud2
# topic: /camera_sensor/points
# marking: true
# clearing: true # STVL can often use a single source for marking/clearing PointCloud2
# obstacle_range: 3.0
# min_obstacle_height: 0.05 # Catch low obstacles
# max_obstacle_height: 2.0
# expected_update_rate: 0.0
# observation_persistence: 0.0
# inf_is_valid: false
# filter: "passthrough"
# voxel_min_points: 1
# # Consider adding separate mark/clear sources if clearing isn't effective
# Configuration for the InflationLayer (runs last)
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
cost_scaling_factor: 3.0
inflation_radius: 0.55 # Adjust based on robot size and environment
always_send_full_costmap: True # Useful for debugging
local_costmap:
local_costmap:
ros__parameters:
use_sim_time: True
update_frequency: 5.0
publish_frequency: 2.0
global_frame: odom
robot_base_frame: base_link
rolling_window: true
width: 8
height: 8
resolution: 0.05
# footprint: "[ [0.254, 0.2159], [0.254, -0.2159], [-0.254, -0.2159], [-0.254, 0.2159] ]"
# footprint_padding: 0.1
robot_radius: 0.12
track_unknown_space: false
plugins: ["spatio_temporal_voxel_layer", "inflation_layer"]
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
cost_scaling_factor: 3.0
inflation_radius: 0.5
spatio_temporal_voxel_layer:
plugin: "spatio_temporal_voxel_layer/SpatioTemporalVoxelLayer"
enabled: true
voxel_decay: 15.
decay_model: 0
voxel_size: 0.05
track_unknown_space: true
unknown_threshold: 15
mark_threshold: 0
update_footprint_enabled: true
combination_method: 1
origin_z: 0.0
publish_voxel_map: false
transform_tolerance: 0.2
mapping_mode: false
map_save_duration: 60.0
observation_sources: depth_cam
# # lidar:
# data_type: PointCloud2
# topic: /sensors/lidar_0/segmented_points
# marking: true
# clearing: true
# obstacle_range: 8.0
# min_obstacle_height: 0.254
# max_obstacle_height: 0.8
# expected_update_rate: 0.0
# observation_persistence: 0.0
# inf_is_valid: false
# filter: "voxel"
# voxel_min_points: 0
# clear_after_reading: true
# max_z: 8.0
# min_z: 0.3
# vertical_fov_angle: 0.785398
# vertical_fov_padding: 0.05
# horizontal_fov_angle: 6.29
# decay_acceleration: 15.0
# model_type: 1
depth_cam:
data_type: PointCloud2
topic: /camera_sensor/points
marking: true
clearing: true
obstacle_range: 2.0
min_obstacle_height: 0.1524
max_obstacle_height: 0.8
expected_update_rate: 0.0
observation_persistence: 0.0
inf_is_valid: false
filter: "voxel"
voxel_min_points: 0
clear_after_reading: true
max_z: 2.0
min_z: 0.2
vertical_fov_angle: 1.047
horizontal_fov_angle: 1.221
decay_acceleration: 15.0
model_type: 0
always_send_full_costmap: True -
@realez 暂时我也没测试过这个，蹲后续
