What problem does it solve? A single mapping run often cannot cover an entire environment, and multiple robots or sessions produce separate maps that must be combined. This Skill provides concrete techniques for merging partial maps into a single coherent map without losing pose graph structure. ## Core Features & Use Cases - SLAM Toolbox Deserialization: Serialize a partial pose graph, then continue mapping in a new session that loads the graph and extends it. - multirobot_map_merge: Merge OccupancyGrid maps from multiple sources with known or automatically estimated initial poses. - Manual Image Merging: Align and merge PGM/PNG map images in GIMP with correct resolution matching and origin recalculation. - Cartographer Multi-Trajectory: Run multiple trajectories against one shared pose graph with inter-trajectory loop closures. - Use Case: You mapped a building's east wing on Monday and the west wing on Tuesday. Use SLAM Toolbox deserialization to load Monday's serialized graph, start the robot inside the mapped area, and continue mapping into the west wing to produce one unified map. ## Quick Start Ask the AI to merge two SLAM Toolbox maps from separate mapping sessions by serializing the first map and continuing mapping from it in a second session.
