📍 Cartographer Relocalization and Pure Localization Autonomous Navigation

1. Course Content

• Master using Google Cartographer to load previously built pose state file (.pbstream) for high-precision global pure localization (Pure Localization).

• Combine Cartographer pure localization pose output with Nav2 path planner to achieve autonomous obstacle-avoiding single-point/multi-point navigation in known map environments.

2. Cartographer Pure Localization Navigation Principle Analysis

In actual industrial and research applications, after map building is complete, there is usually no need to continue incrementally expanding the map during navigation. Cartographer pure localization mode has the following characteristics and advantages:

1. Pose Graph Freeze (Freeze State): After loading existing .pbstream offline pose graph, Cartographer sets all historical submaps to read-only frozen state, no longer inserting new pose nodes and constraint edges, greatly reducing memory usage and CPU computational load.

2. Real-time CSM Scan Matching and Kidnap Resistance: The front-end uses Correlative Scan Matching to quickly perform graph matching between real-time laser scans and historical submaps; even if the robot's starting position deviates or it is short-distance transported (robot kidnapping), the algorithm can quickly re-converge localization based on surrounding geometric features at millisecond level.

3. Seamless Nav2 Navigation Protocol Stack Integration: Cartographer outputs authoritative map -> odom coordinate transformation, while Nav2 focuses on costmap (Costmap 2D) updates, A* global path planning, and local obstacle avoidance control.

3. Preparation and Running

3.1 Start Chassis + Lidar

Run in Terminal 1 (one-click start of A1 Pro chassis driver, IMU attitude solution, and lidar):

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ros2 launch yahboomcar_nav laser_bringup_launch.py

3.2 Start RViz Navigation 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_nav_launch.py

3.3 Start Cartographer Pure Localization Node

Run in Terminal 3 to load .pbstream pose graph and start pure localization core:

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ros2 launch yahboomcar_nav localization_imu_odom.launch.py use_rviz:=false load_state_filename:=$HOME/yahboomcar_ros2_ws/yahboomcar_ws/src/yahboomcar_nav/maps/map.pbstream

3.4 Start Nav2 Navigation Controller

Run in Terminal 4 to start navigation planning and controller (loads corresponding static raster map):

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ros2 launch yahboomcar_nav navigation_cartodwb_launch.py maps:=$HOME/yahboomcar_ros2_ws/yahboomcar_ws/src/yahboomcar_nav/maps/map.yaml

4. Relocalization Navigation Operation Flow and Effect Description

4.1 Automatic Relocalization and Status Check

After Cartographer pure localization starts, the system will automatically match the current laser point cloud with the loaded historical submaps, outputting stable map -> odom coordinate transformation. Observe that the laser point cloud in RViz is completely attached to the map contours, representing successful relocalization.

4.2 Single-Point Autonomous Obstacle-Avoiding Navigation

1. Click the 2D Goal Pose (or Nav2 Goal) tool at the top of RViz2.

2. Click in the free traversable area on the map and drag out the expected goal heading arrow.

3. The system will immediately generate a global planned path, the robot autonomously starts front-wheel steering and smoothly moves forward, real-time avoiding dynamic obstacles during travel and heading to the destination.

4.3 Multi-Point Autonomous Continuous Cruising

If multi-point cruising task is needed:

1. Click Panels / navigation2 /Waypoint / Nav Through Poses Mode in the Navigation2 control panel in the lower-left corner of RViz2.

2. Use Nav2 Goal to sequentially click and calibrate multiple target waypoints on the map.

3. Click Start Waypoint Following, and the robot will sequentially pass through each waypoint in the preset order.

4.4 Goal Arrival and Terminal Feedback

After the robot arrives at the preset goal and completes pose alignment, it automatically stops smoothly, and the terminal will output goal successful completion prompt:

5. Core Source Code and Localization Parameter Analysis

5.1 Pure Localization and Navigation Launch Analysis

Pure Localization Launch Path:

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$HOME/yahboomcar_ros2_ws/yahboomcar_ws/src/yahboomcar_nav/launch/nav2/localization_imu_odom.launch.py

• Loads cartographer_node, loads .pbstream format pose graph through -load_state_filename parameter.

• Starts cartographer_occupancy_grid_node to output static raster map layer.

• Starts set_initpose_from_rviz node to receive /initialpose topic from RViz, achieving interactive relocalization initialization.

Navigation Controller Launch Path:

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$HOME/yahboomcar_ros2_ws/yahboomcar_ws/src/yahboomcar_nav/launch/nav2/navigation_cartodwb_launch.py

• Introduces cartographer_bringup_launch.py, shields AMCL node, with Cartographer providing global localization.

• Loads cartoteb_nav_params.yaml navigation parameter file, collaborating Planner and Controller to complete motion planning.

5.2 Core Algorithm Parameter Interpretation

Parameter Configuration File Path:

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$HOME/yahboomcar_ros2_ws/yahboomcar_ws/src/yahboomcar_nav/params/cartoteb_nav_params.yaml

Core Parameter Description Table

Parameter File Core Configuration Snippet

# Controller server configuration (TEB Local Planner)
controller_server:
  ros__parameters:
    use_sim_time: False
    controller_frequency: 20.0
    FollowPath:
      plugin: "teb_local_planner::TebLocalPlannerROS"
      max_vel_x: 0.30              # Maximum cruising speed (m/s)
      min_vel_x: -0.05             # Limit reverse speed
      max_vel_theta: 1.0           # Maximum turning angular velocity (rad/s)
      acc_lim_x: 1.0               # Linear acceleration limit
      acc_lim_theta: 1.0           # Angular acceleration limit
      min_obstacle_dist: 0.1       # Minimum obstacle hard distance (m)
      xy_goal_tolerance: 0.20      # Arrival distance accuracy (m)
      yaw_goal_tolerance: 0.25     # Arrival angle accuracy (rad)

# Local costmap inflation configuration
local_costmap:
  local_costmap:
    ros__parameters:
      inflation_layer:
        plugin: "nav2_costmap_2d::InflationLayer"
        cost_scaling_factor: 8.0
        inflation_radius: 0.21     # Inflation radius (m)