A Python-based utility for Visual Inertial Odometry data processing, SE(3) matrix conversion, and 3D trajectory visualization.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install tools-2
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install tools-2 using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Pywayne VIO Tools?
Pywayne VIO Tools is a specialized technical utility designed for developers working with robotics, computer vision, and Visual Inertial Odometry (VIO). This skill simplifies the complex task of handling SE(3) transformation matrices and pose representations. By integrating these tools via Openclaw Skills, users can bridge the gap between mathematical transformations and visual data interpretation without manual overhead.
At its core, the tool provides robust functions for bidirectional conversion between 4x4 SE(3) matrices and 7-element pose vectors comprising translation and Hamilton quaternions. Furthermore, it offers built-in 3D visualization capabilities using Matplotlib, allowing researchers to inspect spatial trajectories with clear orientation indicators for the X, Y, and Z axes.
Pywayne VIO Tools Use Cases
- Converting SLAM or VIO output matrices into compact pose representations for efficient storage and transmission.
- Debugging robotic trajectories by visualizing orientation and position markers in 3D space.
- Transforming large datasets from SE(3) matrix format to pose vectors for machine learning training pipelines.
- Analyzing sensor fusion results where precise quaternion-based orientation is required for navigation.
How Pywayne VIO Tools Works
- The user provides either a single 4x4 SE(3) matrix or a NumPy array of multiple matrices representing spatial transformations.
- The skill extracts translation vectors (tx, ty, tz) and computes quaternions (qw, qx, qy, qz) using the Hamilton convention via the qmt dependency.
- For visualization tasks, the tool maps the translation coordinates as position markers in a 3D coordinate system.
- Orientation arrows are generated at each position marker, color-coded by axis (Red for X, Green for Y, Blue for Z) to represent the pose's orientation in real-time.
Pywayne VIO Tools Setup
To get started with this skill on Openclaw Skills, ensure you have the necessary dependencies installed via pip:
pip install numpy qmt ahrs matplotlib
Once the environment is ready, you can import the core functions SE3_to_pose, pose_to_SE3, and visualize_pose directly into your Python scripts.
Pywayne VIO Tools Data Schema & Taxonomy
The skill operates on two primary data structures to ensure compatibility across different robotics and computer vision frameworks:
| Format | Description | Internal Structure |
|---|---|---|
| SE(3) Matrix | 4x4 Transformation Matrix | A homogeneous matrix containing rotation (R) and translation (T). |
| Pose Representation | 7-element Array | [tx, ty, tz, qw, qx, qy, qz] where quaternions follow the Hamilton convention. |
All units for translation are typically processed in meters, while rotation is handled with high precision via the qmt and ahrs libraries.
Pywayne VIO Tools Advanced Features
- Batch processing support for handling thousands of poses simultaneously using optimized NumPy vectorization.
- Customizable visualization parameters, such as the arrow_length_ratio, to adjust the scale of orientation indicators for clearer analysis.
- Full support for the Hamilton quaternion convention, ensuring industry-standard compatibility for aerospace and robotics applications.
- Lightweight architecture designed for seamless integration into larger Openclaw Skills automation workflows and data processing pipelines.
SKILL.md
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