slam-toolbox Mapping Algorithm

1. Course Content

Basics: run the demo program and use the slam-toolbox mapping algorithm to build and save an occupancy grid map.

Advanced: understand the configuration parameters of slam-toolbox

2. slam-toolbox Introduction

slam-toolbox is an open-source ROS-based 2D SLAM (Simultaneous Localization and Mapping) algorithm package, mainly used for mapping and localization of mobile robots in unknown environments. It is based on the Graph Optimization framework and combines sensor data such as LiDAR and IMU to build a 2D occupancy grid map of the environment and update the robot pose in real time.

2.1 Core Technical Principles

2.1.1 Front-end Processing (Laser Scan Matching)

• GICP algorithm: it estimates the relative pose change of the robot by iteratively computing the optimal match between the current laser scan and the map. Compared with traditional ICP, GICP introduces a probabilistic model and is more robust to outliers.
• Motion compensation: uses IMU data to compensate for laser scan distortion during robot motion, improving matching accuracy in dynamic environments.

2.1.2 Back-end Optimization (Graph Optimization)

• Pose graph construction: the robot pose nodes and the relative pose constraints obtained from scan matching form a graph structure.
• Global optimization: uses a nonlinear optimization algorithm (such as Ceres Solver) to optimize the pose graph, eliminating accumulated errors and achieving global consistency of the map.

3. Running the Demo

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ros2 launch microros_v2_bringup slam_toolbox.launch.py

3.1 Mapping Process

• Control the robot to move. You can use keyboard control or gamepad control; keyboard control is used here as an example.

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ros2 run common_demo keyboard_control

• Press I, <, J, L to control the robot to move forward, backward, turn left, and turn right respectively; move the robot slowly to complete mapping

3.2 Saving the Occupancy Grid Map

• Run the following command in the terminal to save the map

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ros2 launch microros_v2_bringup save_map.launch.py

[!TIP]

Optional parameters:

map_name: specify the name of the map to save; the default value is map

For example, to save a map named test.yaml, add the parameter: ros2 launch microros_v2_bringup save_map.launch.py map_name:=test

A Map saved successfully message in the terminal means the map was saved successfully.

3.3 Saving a Pose Graph in posegraph Format

• The posegraph pose graph can be used for route network navigation in later chapters and for navigation using the slam_toolbox localization mode

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ros2 launch microros_v2_bringup save_map.launch.py map_type:=posegraph

6. Configuration Parameters

• Parameter file path for synchronous mapping mode:

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$HOME/microros_ws/src/microros_v2_bringup/config/mapper_params_online_async.yaml

• See the comments for the meaning of each parameter

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sync_slam_toolbox:
  ros__parameters:
​
    # Plugin parameters (optimization    solver_plugin: solver_plugins::CeresSolver  # Optimization solver plugin, uses Ceres solver
    ceres_linear_solver: SPARSE_NORMAL_CHOLESKY  # Ceres linear solver type: sparse normal Cholesky decomposition
    ceres_preconditioner: SCHUR_JACOBI  # Ceres preconditioner type: Schur-Jacobi preconditioning
    ceres_trust_strategy: LEVENBERG_MARQUARDT  # Ceres trust region strategy: Levenberg-Marquardt method
    ceres_dogleg_type: TRADITIONAL_DOGLEG  # Ceres dogleg type: traditional dogleg (only effective when the trust region strategy is DOGLEG)
    ceres_loss_function: None  # Ceres loss function: none (uses the default squared loss)
​
    # ROS parameters
    odom_frame: odom  # Odometry frame name
    map_frame: map  # Map frame name
    base_frame: base_footprint  # Robot base frame name
    scan_topic: /scan  # LiDAR scan data topic (absolute path)
    use_map_saver: true  # Whether to enable the map saver service and subscribe to the map topic
    mode: mapping  # Operating mode: mapping or localization
​
    # Map loading related parameters (if enabled, loads a map from the specified file and continues mapping)
    map_file_name: ""  # Pose graph file name to load (empty means no loading)
    # map_start_pose: [0.0, 0.0, 0.0]  # Starting pose [x, y, yaw] when loading the map (mutually exclusive with map_start_at_dock; this parameter takes priority)
    map_start_at_dock: true  # Whether to start loading from the first node of the map (dock position)
​
    debug_logging: false  # Whether to enable debug log output
    throttle_scans: 1  # Scan data throttle count in synchronous mode (process 1 of every n frames)
    transform_publish_period: 0.02  # Publish period of the map-to-odometry transform (s); 0 means no publishing
    map_update_interval: 5.0  # Update interval of the 2D occupancy grid map (s, for visualization or other applications)
    resolution: 0.05  # Resolution of the 2D occupancy grid map (m/pixel)
    min_laser_range: 0.05  # Minimum valid laser distance used for grid map generation (m)
    max_laser_range: 12.0  # Maximum valid laser distance used for grid map generation (m)
    minimum_time_interval: 0.5  # Minimum time interval for processing two scan frames in synchronous mode (s)
    transform_timeout: 0.2  # Timeout for TF transform lookups (s)
    tf_buffer_duration: 30.  # TF message cache duration (s)
    stack_size_to_use: 40000000  # Program stack size (bytes), used for serialization/deserialization of large maps
    enable_interactive_mode: true  # Whether to enable interactive mode (keeps the laser scan cache for RViz interaction; increases memory usage)
​
    # General parameters
    use_scan_matching: true  # Whether to use scan matching
    use_scan_barycenter: true  # Whether to use the scan barycenter (improves matching stability)
    minimum_travel_distance: 0.5  # Minimum distance the robot must travel before processing new scan data (m)
    minimum_travel_heading: 0.5  # Minimum angle the robot must rotate before processing new scan data (radians)
    scan_buffer_size: 10  # Scan data buffer size (keeps the most recent n scan frames)
    scan_buffer_maximum_scan_distance: 10.0  # Maximum distance of laser points in the scan buffer (m, points beyond this are filtered)
    link_match_minimum_response_fine: 0.1  # Minimum response of link matching in fine matching (used for constraints between adjacent nodes)
    link_scan_maximum_distance: 1.5  # Maximum distance between adjacent scans (m, used to build links between nodes)
    loop_search_maximum_distance: 3.0  # Maximum search distance for loop closure detection (m)
    do_loop_closing: true  # Whether to enable loop closure detection
    loop_match_minimum_chain_size: 10  # Minimum node chain length required for loop closure detection (avoids false matches from short chains)
    loop_match_maximum_variance_coarse: 3.0  # Maximum variance threshold during coarse matching (used to filter candidate loop closures)
    loop_match_minimum_response_coarse: 0.35  # Minimum response of loop closures in coarse matching
    loop_match_minimum_response_fine: 0.45  # Minimum response of loop closures in fine matching
​
    # Correlation parameters - scan matching parameters
    correlation_search_space_dimension: 0.5  # Search space size for scan matching (m, searches for the best match within this range)
    correlation_search_space_resolution: 0.01  # Resolution of the scan matching search space (m, affects matching accuracy and speed)
    correlation_search_space_smear_deviation: 0.1  # Smear deviation of laser points in scan matching (m, increases robustness)
​
    # Correlation parameters - loop closure detection parameters
    loop_search_space_dimension: 8.0  # Search space size for loop closure detection (m)
    loop_search_space_resolution: 0.05  # Resolution of the loop closure search space (m)
    loop_search_space_smear_deviation: 0.03  # Smear deviation of laser points in loop closure detection (m)
​
    # Scan matcher parameters
    distance_variance_penalty: 0.5  # Distance variance penalty coefficient (affects the weight of distance errors in matching)
    angle_variance_penalty: 1.0  # Angle variance penalty coefficient (affects the weight of angle errors in matching)
​
    fine_search_angle_offset: 0.00349  # Angle offset for fine search (radians, about 0.2 deg)
    coarse_search_angle_offset: 0.349  # Angle offset for coarse search (radians, about 20 deg)
    coarse_angle_resolution: 0.0349  # Angular resolution for coarse search (radians, about 2 deg)
    minimum_angle_penalty: 0.9  # Minimum angle penalty coefficient (filters matches with excessive angle deviation)
    minimum_distance_penalty: 0.5  # Minimum distance penalty coefficient (filters matches with excessive distance deviation)
    use_response_expansion: true  # Whether to use response expansion (improves matching stability)
    min_pass_through: 2  # Minimum number of points passing through in scan matching (filters invalid matches)
    occupancy_threshold: 0.1  # Occupancy grid threshold (used to determine whether a cell is occupied)