# slam_toolbox — online async SLAM configuration
# Jetson Orin Nano Super — all Nano rate caps removed.
#
# Primary SLAM is now RTAB-Map (slam_rtabmap.launch.py).
# slam_toolbox retained for LIDAR-only localization against pre-built maps.
#
# Input:  /scan  (LaserScan from RPLIDAR A1M8, ~5.5 Hz)
# Output: /map   (OccupancyGrid)

slam_toolbox:
  ros__parameters:
    odom_frame:   odom
    map_frame:    map
    base_frame:   base_link
    scan_topic:   /scan
    mode:         mapping

    resolution:               0.05
    max_laser_range:          8.0
    map_update_interval:      1.0     # 1s (was 5s on Nano)
    minimum_travel_distance:  0.2
    minimum_travel_heading:   0.2

    minimum_time_interval:  0.1    # ~10Hz processing (was 0.5s on Nano)
    transform_timeout:      0.2
    tf_buffer_duration:     30.0
    stack_size_to_use:      40000000
    enable_interactive_mode: false

    use_scan_matching:    true
    use_scan_barycenter:  true
    scan_buffer_size:     20
    scan_buffer_maximum_scan_distance: 10.0

    do_loop_closing:                      true
    loop_match_minimum_chain_size:        10
    loop_match_maximum_variance_coarse:   3.0
    loop_match_minimum_response_coarse:   0.35
    loop_match_minimum_response_fine:     0.45
    loop_search_maximum_distance:         3.0

    correlation_search_space_dimension:       0.5
    correlation_search_space_resolution:      0.01
    correlation_search_space_smear_deviation: 0.1

    loop_search_space_dimension:       8.0
    loop_search_space_resolution:      0.05
    loop_search_space_smear_deviation: 0.03

    link_match_minimum_response_fine: 0.1
    link_scan_maximum_distance:       1.5
    use_response_expansion:           true
    distance_variance_penalty:        0.5
    angle_variance_penalty:           1.0
    fine_search_angle_offset:         0.00349
    coarse_search_angle_offset:       0.349
    coarse_angle_resolution:          0.0349
    minimum_angle_penalty:            0.9
    minimum_distance_penalty:         0.5

    solver_plugin:           solver_plugins::CeresSolver
    ceres_linear_solver:     SPARSE_NORMAL_CHOLESKY
    ceres_preconditioner:    SCHUR_JACOBI
    ceres_trust_strategy:    LEVENBERG_MARQUARDT
    ceres_dogleg_type:       TRADITIONAL_DOGLEG
    ceres_loss_function:     None