saltylab-firmware/jetson/config/slam_toolbox_params.yaml
sl-perception 76067d6d89 feat(bd-a2j): RealSense D435i + RPLIDAR A1M8 ROS2 driver integration
Adds saltybot_bringup ROS2 package with four launch files:
  - realsense.launch.py  — D435i at 640x480x15fps, IMU unified topic
  - rplidar.launch.py    — RPLIDAR A1M8 via /dev/rplidar udev symlink
  - sensors.launch.py    — both sensors + static TF (base_link→laser/camera)
  - slam.launch.py       — sensors + slam_toolbox online_async (compose entry point)

Sensor config YAMLs (mounted at /config/ in container):
  - realsense_d435i.yaml  — Nano power-budget settings (15fps, no pointcloud)
  - rplidar_a1m8.yaml     — Standard scan mode, 115200 baud, laser frame
  - slam_toolbox_params.yaml — Nano-tuned (2Hz processing, 5cm resolution)

Fixes docker-compose volume mount: ./ros2_ws/src:/ros2_ws/src
(was ./ros2_ws:/ros2_ws/src — would have double-nested the src directory)

Topic reference and verification commands in SENSORS.md.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-02-28 17:14:21 -05:00

80 lines
4.5 KiB
YAML

# slam_toolbox — online async SLAM configuration
# Tuned for Jetson Nano 4GB (constrained CPU/RAM, indoor mapping)
#
# Input: /scan (LaserScan from RPLIDAR A1M8, ~5.5 Hz)
# Output: /map (OccupancyGrid, updated every map_update_interval seconds)
#
# Frame assumptions (must match sensors.launch.py static TF):
# map → odom → base_link → laser
# (odom not yet published — slam_toolbox handles this via scan matching)
slam_toolbox:
ros__parameters:
# ── Frames ───────────────────────────────────────────────────────────────
odom_frame: odom
map_frame: map
base_frame: base_link
scan_topic: /scan
mode: mapping # 'mapping' or 'localization'
# ── Map params ───────────────────────────────────────────────────────────
resolution: 0.05 # 5 cm/cell — good balance for A1M8 angular res
max_laser_range: 8.0 # clip to reliable range of A1M8 (spec: 12m)
map_update_interval: 5.0 # seconds between full map publishes (saves CPU)
minimum_travel_distance: 0.3 # only update after moving 30 cm
minimum_travel_heading: 0.3 # or rotating ~17°
# ── Performance (Nano-specific) ───────────────────────────────────────────
# Reduce scan processing rate to stay within ~3.5W CPU budget
minimum_time_interval: 0.5 # max 2 Hz scan processing (A1M8 is ~5.5 Hz)
transform_timeout: 0.2
tf_buffer_duration: 30.0
stack_size_to_use: 40000000 # 40 MB stack
enable_interactive_mode: false # disable interactive editing (saves CPU)
# ── Scan matching ─────────────────────────────────────────────────────────
use_scan_matching: true
use_scan_barycenter: true
scan_buffer_size: 10
scan_buffer_maximum_scan_distance: 10.0
# ── Loop closure ──────────────────────────────────────────────────────────
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 (coarse scan matching) ───────────────────────────────────
correlation_search_space_dimension: 0.5
correlation_search_space_resolution: 0.01
correlation_search_space_smear_deviation: 0.1
# ── Loop search space ─────────────────────────────────────────────────────
loop_search_space_dimension: 8.0
loop_search_space_resolution: 0.05
loop_search_space_smear_deviation: 0.03
# ── Response expansion ────────────────────────────────────────────────────
link_match_minimum_response_fine: 0.1
link_scan_maximum_distance: 1.5
use_response_expansion: true
# ── Penalties (scan matcher quality thresholds) ───────────────────────────
distance_variance_penalty: 0.5
angle_variance_penalty: 1.0
fine_search_angle_offset: 0.00349 # ~0.2°
coarse_search_angle_offset: 0.349 # ~20°
coarse_angle_resolution: 0.0349 # ~2°
minimum_angle_penalty: 0.9
minimum_distance_penalty: 0.5
# ── Solver ────────────────────────────────────────────────────────────────
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