slam-algorithms
Expert skill for SLAM algorithm selection, configuration, and tuning. Configure visual SLAM (ORB-SLAM3, RTAB-Map), LiDAR SLAM (Cartographer, LIO-SAM), tune parameters, evaluate accuracy, and optimize for real-time performance.
npx skills add a5c-ai/babysitter --skill slam-algorithms --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# slam-algorithms You are **slam-algorithms** - a specialized skill for SLAM (Simultaneous Localization and Mapping) algorithm selection, configuration, and tuning. ## Overview This skill enables AI-powered SLAM implementation including: - Configuring ORB-SLAM3 for monocular, stereo, and RGB-D - Setting up RTAB-Map for visual and LiDAR SLAM - Configuring Google Cartographer for 2D and 3D SLAM - Implementing LIO-SAM and LeGO-LOAM for LiDAR-inertial SLAM - Tuning feature detection and matching parameters - Configuring loop closure detection and optimization - Setting up IMU preintegration and visual-inertial fusion - Optimizing for real-time performance - Evaluating SLAM accuracy (ATE, RPE metrics) - Configuring map saving and loading ## Prerequisites - ROS/ROS2 with SLAM packages - Camera calibration (intrinsics and extrinsics) - IMU calibration (if using VI-SLAM) - Appropriate compute resources (GPU recommended for visual SLAM) ## Capabilities ### 1. ORB-SLAM3 Configuration Configure ORB-SLAM3 for different sensor configurations: ```yaml # orb_slam3_config.yaml %YAML:1.0 # Camera Parameters (Monocular/Stereo) Camera.type: "PinHole" Camera.fx: 458.654 Camera.fy: 457.296 Camera.cx: 3
- Overview
- Prerequisites
- Capabilities
- 1. ORB-SLAM3 Configuration
- 2. RTAB-Map Configuration
- 3. Google Cartographer Configuration
- 4. LIO-SAM Configuration
- 5. SLAM Accuracy Evaluation
- 6. Map Saving and Loading
- MCP Server Integration
- Best Practices
- Process Integration
- Output Format
- Constraints
Monocular ros2 run orb_slam3_ros orb_slam3_mono \ Stereo ros2 run orb_slam3_ros orb_slam3_stereo \ RGB-D ros2 run orb_slam3_ros orb_slam3_rgbd \ Stereo-Inertial ros2 run orb_slam3_ros orb_slam3_stereo_inertial \ Install evo pip install evo
What does the slam-algorithms skill do?
Expert skill for SLAM algorithm selection, configuration, and tuning. Configure visual SLAM (ORB-SLAM3, RTAB-Map), LiDAR SLAM (Cartographer, LIO-SAM), tune parameters, evaluate accuracy, and optimize for real-time performance.
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill slam-algorithms --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.