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slam-toolbox-lifelong

Configure SLAM Toolbox lifelong mode for persistent mapping with graph trimming and map deserialization.

Updated Aug 16, 2026
Search Tags:#ros2#robot navigation#loop closure#slam toolbox#lifelong mapping#pose graph#map serialization
One-click install
npx skills add https://github.com/three1324/yeonjinautomotive --skill slam-toolbox-lifelong-three1324
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: slam-toolbox-lifelong
Source: https://github.com/three1324/yeonjinautomotive/tree/main/.claude/skills/slam-toolbox-lifelong
Command: npx skills add https://github.com/three1324/yeonjinautomotive --skill slam-toolbox-lifelong-three1324

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Robots that run for days or weeks accumulate ever-growing pose graphs that consume memory, slow the solver, and retain stale map data. This Skill guides the configuration of SLAM Toolbox lifelong mode so maps stay current and bounded over long deployments. ## Core Features & Use Cases - Lifelong Plugin Configuration: Set up slam_toolbox::LifelongSlamToolbox with a complete YAML covering scan matching, loop closure, and the Ceres solver. - Map Serialization Workflow: Serialize an initial online-mode map to .posegraph and .data files, then deserialize it as the starting point for lifelong operation. - Graph Trimming & Map Evolution: Understand how old low-information nodes are trimmed automatically and how revisited areas get updated when the environment changes. - Use Case: A patrol robot revisits a warehouse daily. Start from a serialized map, let lifelong mode reflect moved furniture and opened doors, and periodically re-serialize to save the latest state. ## Quick Start Ask the assistant to generate a lifelong mode YAML configuration for SLAM Toolbox that loads a previously serialized map and enables automatic graph trimming.

Frequently Asked Questions about slam-toolbox-lifelong

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up SLAM Toolbox lifelong mode in ROS2?▼

Set the plugin parameter to slam_toolbox::LifelongSlamToolbox in your YAML config, point map_file_name to a serialized map base name, and set map_start_pose with map_start_at_dock. The node loads the graph on startup and continues mapping from it.

When should I use lifelong mode vs online mode in SLAM Toolbox?▼

Use online mode for first-time mapping of a new environment or one-shot mapping before switching to Nav2 AMCL. Use lifelong mode when the robot runs 8+ hours continuously, the environment changes frequently, or you need to update a saved map periodically.

How do I serialize and reload a SLAM Toolbox map?▼

Call the /slam_toolbox/serialize_map service with a filename to write .posegraph and .data files. To reload, set map_file_name to that base name in the lifelong config; both files must share the same directory and base name.

Why does SLAM Toolbox take long to start with a large map?▼

Deserializing a large graph with more than 5000 nodes can take 10-30 seconds, and the node will not publish TF until loading completes. This is expected behavior for large serialized maps.

Why does SLAM Toolbox crash during optimization on large graphs?▼

Crashes during optimization on large graphs are typically caused by insufficient stack size. Increase the stack_size_to_use parameter from the default 40000000 to 80000000 (80MB) to give the solver enough memory.

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