🗺️ Cartographer-SLAM Mapping

1. Course Content

• Master using Google Cartographer algorithm for 2D laser SLAM mapping.

• Master remote controlling the robot for mapping and viewing submap and global loop closure optimization effects in RViz2.

• Master methods for saving standard OccupancyGrid raster maps (.yaml + .pgm) and Cartographer pose graph state files (.pbstream).

2. Cartographer Introduction

Cartographer is a 2D/3D SLAM system open-sourced by Google based on Graph-based SLAM framework:

• Submap Construction (Front-End): Continuous multiple frames of LaserScan laser data are inserted through correlative scan matching (CSM) to construct local Submap submaps.

• Loop Closure Detection and Back-End Optimization (Back-End): When the number of constructed submaps increases, the back-end performs loop closure constraint solving (Loop Closure) multi-threaded via Ceres solver, eliminating accumulated drift from odometry and scan matching.

• Features and Advantages: Extremely high mapping accuracy, strong loop closure correction ability, supports building highly consistent maps in large-scale indoor complex environments.

3. Preparation and Running

This example requires opening 3~4 terminals to start the following nodes in sequence:

3.1 Start Cartographer Mapping Node

Run in Terminal 1 (automatically starts chassis driver, lidar driver, and Cartographer mapping core):

ros2 launch yahboomcar_nav map_cartographer_launch.py

3.2 Start RViz Map Visualization

Run in Terminal 2 (it is recommended to open RViz on the virtual machine if the VM and robot are on the same LAN):

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ros2 launch yahboomcar_nav display_map_launch.py

3.3 Start Keyboard Control to Move Robot for Mapping

Run in Terminal 3 the keyboard control node:

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ros2 run yahboomcar_ctrl yahboom_keyboard

3.4 Mapping Operation and Map Save Instructions

1. Mapping Driving Suggestions:

   • Remote control the robot to slowly and smoothly drive through the entire environment, observe the continuous splicing of submaps in RViz.

   • When completing a loop back to the starting origin, the back-end will automatically match historical submaps and trigger global loop closure optimization, instantly eliminating corridor accumulated drift.

2. Save 2D Raster Map (.yaml + .pgm): Run the map save command in Terminal 4:

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   ros2 launch yahboomcar_nav save_map_launch.py map_name:=map
   

   

   Map files are saved in ~/yahboomcar_ros2_ws/yahboomcar_ws/src/yahboomcar_nav/maps/ directory.

3. Save Cartographer Pose Graph File (.pbstream): The pose map (.pbstream) contains complete graph-optimized pose nodes and original constraint data, which is the required file for subsequent Cartographer pure localization relocalization navigation:

   • Finish current mapping trajectory:

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     ros2 service call /finish_trajectory cartographer_ros_msgs/srv/FinishTrajectory "{trajectory_id: 0}"
     

   • Write and save pose graph file:

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     ros2 service call /write_state cartographer_ros_msgs/srv/WriteState "{filename: '$HOME/yahboomcar_ros2_ws/yahboomcar_ws/src/yahboomcar_nav/maps/map.pbstream'}"
     

     

4. Core Source Code and Algorithm Parameter Analysis

4.1 Mapping Launch File Analysis

Launch File Path:

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$HOME/yahboomcar_ros2_ws/yahboomcar_ws/src/yahboomcar_nav/launch/slam/map_cartographer_launch.py

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def generate_launch_description():
    # Introduce chassis and lidar driver
    laser_bringup_launch = IncludeLaunchDescription(PythonLaunchDescriptionSource(
        [os.path.join(get_package_share_directory('yahboomcar_nav'), 'launch','robot'),
         '/laser_bringup_no_odom_launch.py'])
    )

    # Introduce Cartographer SLAM mapping node
    cartographer_launch = IncludeLaunchDescription(PythonLaunchDescriptionSource(
        [os.path.join(get_package_share_directory('yahboomcar_nav'), 'launch','slam'),
         '/cartographer_launch.py'])
    )

    return LaunchDescription([laser_bringup_launch, cartographer_launch])

• laser_bringup_no_odom_launch.py: Start A1 Pro chassis and lidar driver, avoiding conflicts between external TF and Cartographer's own output TF.

• cartographer_launch.py: Load Cartographer core node cartographer_node and raster conversion node occupancy_grid_node.

4.2 Cartographer Algorithm Configuration and Core Parameter Interpretation

Configuration File Path:

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$HOME/yahboomcar_ros2_ws/yahboomcar_ws/src/yahboomcar_nav/params/lds_2d.lua

Core Parameter Description Table

Lua Configuration File Core Source

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include "map_builder.lua"
include "trajectory_builder.lua"

options = {
  map_builder = MAP_BUILDER,
  trajectory_builder = TRAJECTORY_BUILDER,
  map_frame = "map",
  tracking_frame = "base_footprint",
  published_frame = "base_footprint",
  odom_frame = "odom",
  provide_odom_frame = true,
  publish_frame_projected_to_2d = false,
  use_odometry = false, 
  num_laser_scans = 1,
  lookup_transform_timeout_sec = 0.2,
  submap_publish_period_sec = 0.3,
  pose_publish_period_sec = 5e-3,
  trajectory_publish_period_sec = 30e-3,
}

MAP_BUILDER.use_trajectory_builder_2d = true

TRAJECTORY_BUILDER_2D.min_range = 0.1
TRAJECTORY_BUILDER_2D.max_range = 10.0
TRAJECTORY_BUILDER_2D.missing_data_ray_length = 0.5
TRAJECTORY_BUILDER_2D.use_imu_data = false 
TRAJECTORY_BUILDER_2D.use_online_correlative_scan_matching = true 
TRAJECTORY_BUILDER_2D.motion_filter.max_angle_radians = math.rad(0.1)

-- Ceres loop closure graph optimization matching threshold
POSE_GRAPH.constraint_builder.min_score = 0.7
POSE_GRAPH.constraint_builder.global_localization_min_score = 0.7

return options