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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

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