admin管理员组文章数量:1658729
最近这一段时间一直在调节基于蒙特卡罗定位的导航,前提是基于已经开源的基于激光雷达的SLAM算法。地图的建立,主要是参考网上的开源教程,根据实际情况,调整相关的参数文件。最终是实现了建图。建立的地图的精度还没有具体的分析,但从直观上看,应该是没什么问题。因此计划通过AMCL来测试建立地图的精度。要运行AMCL首要的工作是运行move_base导航包;本文以rplidar_A2雷达为例,介绍AMCL的定位性能。
调节move_base节点,相关的参数文件:
(1) rplidar_amcl.launch.xml
(2) rplidar_costmap_params.yaml
(3) move_base.launch.xml
(4) costmap_common_params.yaml
max_obstacle_height: 2.0 # assume something like an arm is mounted on top of the robot
# Obstacle Cost Shaping (http://wiki.ros/costmap_2d/hydro/inflation)
robot_radius: 0.18 # distance a circular robot should be clear of the obstacle (kobuki: 0.18)
# footprint: [[x0, y0], [x1, y1], ... [xn, yn]] # if the robot is not circular
#map_type: voxel
obstacle_layer:
enabled: true
max_obstacle_height: 2.0
min_obstacle_height: 0.0
#origin_z: 0.0
#z_resolution: 0.2
#z_voxels: 2
#unknown_threshold: 15
#mark_threshold: 0
combination_method: 1
track_unknown_space: true #true needed for disabling global path planning through unknown space
obstacle_range: 2.0
raytrace_range: 5.0
#origin_z: 0.0
#z_resolution: 0.2
#z_voxels: 2
publish_voxel_map: false
observation_sources: scan
scan:
data_type: LaserScan
topic: "/scan"
marking: true
clearing: true
expected_update_rate: 0
#cost_scaling_factor and inflation_radius were now moved to the inflation_layer ns
inflation_layer:
enabled: true
cost_scaling_factor: 10.0 # exponential rate at which the obstacle cost drops off (default: 10)
inflation_radius: 0.25 # max. distance from an obstacle at which costs are incurred for planning paths.
static_layer:
enabled: true
map_topic: "/map"
(5) local_costmap_params.yaml
local_costmap:
global_frame: odom
robot_base_frame: /base_footprint
update_frequency: 5.0
publish_frequency: 2.0
static_map: false
rolling_window: true
width: 4.0
height: 4.0
resolution: 0.05
transform_tolerance: 0.5
plugins:
- {name: obstacle_layer, type: "costmap_2d::ObstacleLayer"}
- {name: inflation_layer, type: "costmap_2d::InflationLayer"}
(6) global_costmap_params.yaml
global_costmap:
global_frame: /map
robot_base_frame: /base_footprint
update_frequency: 1.0
publish_frequency: 0.5
static_map: true
transform_tolerance: 0.5
plugins:
- {name: static_layer, type: "costmap_2d::StaticLayer"}
- {name: obstacle_layer, type: "costmap_2d::VoxelLayer"}
- {name: inflation_layer, type: "costmap_2d::InflationLayer"}
(7) dwa_local_planner_params.yaml
DWAPlannerROS:
# Robot Configuration Parameters - Kobuki
max_vel_x: 0.5 # 0.55
min_vel_x: 0.0
max_vel_y: 0.0 # diff drive robot
min_vel_y: 0.0 # diff drive robot
max_trans_vel: 0.5 # choose slightly less than the base's capability
min_trans_vel: 0.1 # this is the min trans velocity when there is negligible rotational velocity
trans_stopped_vel: 0.1
# Warning!
# do not set min_trans_vel to 0.0 otherwise dwa will always think translational velocities
# are non-negligible and small in place rotational velocities will be created.
max_rot_vel: 5.0 # choose slightly less than the base's capability
min_rot_vel: 0.4 # this is the min angular velocity when there is negligible translational velocity
rot_stopped_vel: 0.4
acc_lim_x: 1.0 # maximum is theoretically 2.0, but we
acc_lim_theta: 2.0
acc_lim_y: 0.0 # diff drive robot
# Goal Tolerance Parameters
yaw_goal_tolerance: 0.3 # 0.05
xy_goal_tolerance: 0.15 # 0.10
# latch_xy_goal_tolerance: false
# Forward Simulation Parameters
sim_time: 1.0 # 1.7
vx_samples: 6 # 3
vy_samples: 1 # diff drive robot, there is only one sample
vtheta_samples: 20 # 20
# Trajectory Scoring Parameters
path_distance_bias: 64.0 # 32.0 - weighting for how much it should stick to the global path plan
goal_distance_bias: 24.0 # 24.0 - wighting for how much it should attempt to reach its goal
occdist_scale: 0.5 # 0.01 - weighting for how much the controller should avoid obstacles
forward_point_distance: 0.325 # 0.325 - how far along to place an additional scoring point
stop_time_buffer: 0.2 # 0.2 - amount of time a robot must stop in before colliding for a valid traj.
scaling_speed: 0.25 # 0.25 - absolute velocity at which to start scaling the robot's footprint
max_scaling_factor: 0.2 # 0.2 - how much to scale the robot's footprint when at speed.
# Oscillation Prevention Parameters
oscillation_reset_dist: 0.05 # 0.05 - how far to travel before resetting oscillation flags
# Debugging
publish_traj_pc : true
publish_cost_grid_pc: true
global_frame_id: odom
# Differential-drive robot configuration - necessary?
(8) move_base_params.yaml
# http://www.ros/wiki/move_base
shutdown_costmaps: false
controller_frequency: 5.0
controller_patience: 3.0
planner_frequency: 1.0
planner_patience: 5.0
oscillation_timeout: 10.0
oscillation_distance: 0.2
# local planner - default is trajectory rollout
base_local_planner: "dwa_local_planner/DWAPlannerROS"
base_global_planner: "navfn/NavfnROS" #alternatives: global_planner/GlobalPlanner, carrot_planner/CarrotPlanner
#We plan to integrate recovery behaviors for turtlebot but currently those belong to gopher and still have to be adapted.
## recovery behaviors; we avoid spinning, but we need a fall-back replanning
#recovery_behavior_enabled: true
#recovery_behaviors:
#- name: 'super_conservative_reset1'
#type: 'clear_costmap_recovery/ClearCostmapRecovery'
#- name: 'conservative_reset1'
#type: 'clear_costmap_recovery/ClearCostmapRecovery'
#- name: 'aggressive_reset1'
#type: 'clear_costmap_recovery/ClearCostmapRecovery'
#- name: 'clearing_rotation1'
#type: 'rotate_recovery/RotateRecovery'
#- name: 'super_conservative_reset2'
#type: 'clear_costmap_recovery/ClearCostmapRecovery'
#- name: 'conservative_reset2'
#type: 'clear_costmap_recovery/ClearCostmapRecovery'
#- name: 'aggressive_reset2'
#type: 'clear_costmap_recovery/ClearCostmapRecovery'
#- name: 'clearing_rotation2'
#type: 'rotate_recovery/RotateRecovery'
#super_conservative_reset1:
#reset_distance: 3.0
#conservative_reset1:
#reset_distance: 1.5
#aggressive_reset1:
#reset_distance: 0.0
#super_conservative_reset2:
#reset_distance: 3.0
#conservative_reset2:
#reset_distance: 1.5
#aggressive_reset2:
#reset_distance: 0.0
(9) global_planner_params.yaml
GlobalPlanner: # Also see: http://wiki.ros/global_planner
old_navfn_behavior: false # Exactly mirror behavior of navfn, use defaults for other boolean parameters, default false
use_quadratic: true # Use the quadratic approximation of the potential. Otherwise, use a simpler calculation, default true
use_dijkstra: true # Use dijkstra's algorithm. Otherwise, A*, default true
use_grid_path: false # Create a path that follows the grid boundaries. Otherwise, use a gradient descent method, default false
allow_unknown: true # Allow planner to plan through unknown space, default true
#Needs to have track_unknown_space: true in the obstacle / voxel layer (in costmap_commons_param) to work
planner_window_x: 0.0 # default 0.0
planner_window_y: 0.0 # default 0.0
default_tolerance: 0.0 # If goal in obstacle, plan to the closest point in radius default_tolerance, default 0.0
publish_scale: 100 # Scale by which the published potential gets multiplied, default 100
planner_costmap_publish_frequency: 0.0 # default 0.0
lethal_cost: 253 # default 253
neutral_cost: 50 # default 50
cost_factor: 3.0 # Factor to multiply each cost from costmap by, default 3.0
publish_potential: true # Publish Potential Costmap (this is not like the navfn pointcloud2 potential), default true
(10) navfn_global_planner_params.yaml
NavfnROS:
visualize_potential: false #Publish potential for rviz as pointcloud2, not really helpful, default false
allow_unknown: false #Specifies whether or not to allow navfn to create plans that traverse unknown space, default true
#Needs to have track_unknown_space: true in the obstacle / voxel layer (in costmap_commons_param) to work
planner_window_x: 0.0 #Specifies the x size of an optional window to restrict the planner to, default 0.0
planner_window_y: 0.0 #Specifies the y size of an optional window to restrict the planner to, default 0.0
default_tolerance: 0.0 #If the goal is in an obstacle, the planer will plan to the nearest point in the radius of default_tolerance, default 0.0
#The area is always searched, so could be slow for big values
(11) smoother.yaml
# Default parameters used by the yocs_velocity_smoother module.
# This isn't used by minimal.launch per se, rather by everything
# which runs on top.
# Mandatory parameters
speed_lim_v: 0.8
speed_lim_w: 5.4
accel_lim_v: 1.0 # maximum is actually 2.0, but we push it down to be smooth
accel_lim_w: 2.0
# Optional parameters
frequency: 20.0
decel_factor: 1.5
# Robot velocity feedback type:
# 0 - none (default)
# 1 - odometry
# 2 - end robot commands
robot_feedback: 2
这里面包含了运行amcl所需设定的所以参数,具体参数怎么调节,需要根据实际运行环境以及激光雷达的参数来设定。
本人经过测试在安装激光雷达的时候一定要保证激光雷达的X轴正方向与kobuki行走的正方向保持一致,这是保持运行AMCL时不会撞墙的根本保证。如果将激光雷达安装的不与机器人行走的方向一致,将导致导航定位不准确。
本文标签: 地图蒙特卡罗测试Cartographer
版权声明:本文标题:cartographer 使用 定位_基于gmapping地图与cartographer地图测试蒙特卡罗定位 内容由热心网友自发贡献,该文观点仅代表作者本人, 转载请联系作者并注明出处:https://m.elefans.com/xitong/1729813142a1213571.html, 本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌抄袭侵权/违法违规的内容,一经查实,本站将立刻删除。
发表评论