# Map Merging > --- name: map-merging description: 'Merge multiple maps. Use when combining maps from different sessions, robots, or partial mapping runs.' --- # Map Merging Techniques ## Overview Map merging combines maps from multiple sessions, robots, or partial scans into a single coherent map. This is necessary when a single mapping run cannot cover the entire environment, or when multiple robots map simultaneously. There is no one-size-fits-all solution—the approach depends on whether you have pose graph data, overlapping regions, or only raw images. ## Approach 1: SLAM Toolbox Deserialization + Continue Mapping The most robust approach when using SLAM Toolbox. You serialize a partial map, then start a new session that loads the serialized graph and continues mapping. ### Workflow ```bash # Session 1: Map area A ros2 launch slam_toolbox online_sync_launch.py params_file:=params.yaml # Drive through area A, then serialize: ros2 service call /slam_toolbox/serialize_map slam_toolbox/srv/SerializePoseGraph \ "{filename: '/home/robot/maps/area_a'}" # Session 2: Continue into area B # Configure SLAM Toolbox to load the serialized graph: ``` ```yaml slam_toolbox: ros__parameters: map_file_name: /home/robot/maps/area_a # Base name, no extension. map_start_pose: [0.0, 0.0, 0.0] map_start_at_dock: true mode: mapping ``` ```bash # Start SLAM Toolbox with the above config. # Drive through area B. The graph now contains A + B. # Serialize the combined graph: ros2 service call /slam_toolbox/serialize_map slam_toolbox/srv/SerializePoseGraph \ "{filename: '/home/robot/maps/area_ab'}" ``` ### Requirements - The robot must start session 2 at a location within the area covered by session 1's map. - SLAM Toolbox must successfully match the initial scans against the loaded graph. - If the robot starts outside the mapped area, merging fails silently (no constraints connect old and new nodes). ### Verifying the Merge After deserialization + continued mapping, check the constraint visualization in RViz. There should be constraints connecting old (area A) nodes to new (area B) nodes at the overlap region. ## Approach 2: multirobot_map_merge Package The `multirobot_map_merge` package merges OccupancyGrid maps from multiple sources using feature matching on the map images. ```bash sudo apt install ros-jazzy-multirobot-map-merge # If available for Jazzy. ``` ### Configuration ```yaml map_merge: ros__parameters: robot_map_topic: map robot_namespace: '' merged_map_topic: /merged_map world_frame: map known_init_poses: true # true if you know the relative poses of the robots/sessions. init_pose_x_0: 0.0 init_pose_y_0: 0.0 init_pose_yaw_0: 0.0 init_pose_x_1: 10.0 # Robot 2 starts 10m east of robot 1. init_pose_y_1: 0.0 init_pose_yaw_1: 0.0 merging_rate: 0.5 # Hz. How often to attempt merging. estimation_rate: 0.5 # Hz for transform estimation (if known_init_poses=false). estimation_confidence: 1.0 # Minimum confidence for automatic alignment. ``` ### Known vs Unknown Initial Poses - **known_init_poses: true**: You provide the relative starting positions of each robot/session. The package directly overlays the maps using these transforms. More reliable. - **known_init_poses: false**: The package attempts to find the alignment automatically using feature matching on the occupancy grids. Works if there is sufficient overlap (~30%) and distinctive features. Often fails in symmetric or featureless environments. ### Limitations - Only merges 2D OccupancyGrids—no pose graph, so no correction of internal drift. - Requires significant overlap between maps for automatic alignment. - Quality depends on individual map quality. Distorted maps produce distorted merges. ## Approach 3: Manual Image Merging For simple cases, manually aligning map images in an editor can be effective. ### Steps 1. Open both PGM/PNG maps in GIMP or similar. 2. Identify overlapping features (walls, doorways). 3. Align the second map over the first using translate/rotate. 4. Merge layers: for each pixel, take the most informative value (occupied > free > unknown). 5. Export as PGM. 6. Manually create the YAML metadata file with the correct origin and resolution. ### Resolution Matching **Both maps must have the same resolution.** If they differ, rescale one: ```bash # In GIMP: Image → Scale Image → set width/height to match the resolution ratio. # Or use ImageMagick: convert map_b.pgm -resize 200% map_b_scaled.pgm # If map_b is 0.1m and map_a is 0.05m. ``` ### Origin Alignment After merging, the origin must be recalculated. The new origin is the bottom-left corner of the merged image in map coordinates: ```python # If map_a origin is (-12.2, -10.7) and map_b was shifted by (15.0, 0.0): # New origin depends on which map's bottom-left is further negative. new_origin_x = min(origin_a_x, origin_b_x + shift_x) new_origin_y = min(origin_a_y, origin_b_y + shift_y) ``` ## Approach 4: Cartographer Map Merging Cartographer supports multi-trajectory mapping natively. You can add a second trajectory that shares the same pose graph: ```bash # Start trajectory 1 for robot 1: ros2 service call /start_trajectory cartographer_ros_msgs/srv/StartTrajectory ... # Start trajectory 2 for robot 2 (or second session): ros2 service call /start_trajectory cartographer_ros_msgs/srv/StartTrajectory ... ``` Both trajectories contribute to the same global optimization. The constraint builder finds inter-trajectory loop closures where the trajectories overlap. ## Overlapping Region Requirements For any automated merging to work: | Method | Minimum Overlap | |--------|----------------| | SLAM Toolbox deserialization | Robot must start within mapped area | | multirobot_map_merge (auto) | ~30% shared area with distinctive features | | multirobot_map_merge (known poses) | Any overlap | | Cartographer multi-trajectory | Physical overlap with loop closure opportunity | | Manual (GIMP) | Visual landmarks to align | ## Map Georeferencing For outdoor or multi-building scenarios, maps can be aligned to a global coordinate system (GPS/UTM): 1. Record GPS coordinates at known points during mapping. 2. Compute the affine transform from map coordinates to UTM. 3. Store the transform in the map metadata or a separate config. This enables merging maps from different buildings or areas by aligning them in the shared global frame. ## Best Practices - **Always serialize the full SLAM graph**, not just the image. Images lose internal pose graph structure. - **Test merges incrementally**: merge two maps, verify, then add a third. - **Prefer graph-based merging** (SLAM Toolbox deserialization, Cartographer multi-trajectory) over image-based when possible. - **Validate with loop closures**: after merging, drive the robot through the overlap region to confirm the merge is geometrically correct. - **Version your maps**: keep dated copies of serialized graphs so you can revert a bad merge.