How do I install the Pywayne Vio Tools skill?

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Which AI agents work with Pywayne Vio Tools?

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Is Pywayne Vio Tools free to use?

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Who published Pywayne Vio Tools?

modbender (@modbender) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Pywayne Vio Tools by modbender — Agent Skill

Name: Pywayne Vio Tools
Rating: 4.666666666666667 (3 reviews)
Author: modbender

Pywayne Vio Tools

VIO (Visual Inertial Odometry) data processing utilities for SE(3) pose conversion and 3D visualization. Use when working with SE(3) matrices and need pose representation conversion (tx,ty,tz,qw,qx,qy,qz), 3D visualization of pose trajectories with orientation arrows, or Visual Inertial Odometry data transformation and analysis

modbender Updated Sep 9, 2026 12 repo stars

File contents

Pywayne VIO Tools

Overview

Provides utilities for VIO data processing: convert between SE(3) transformation matrices and pose representations, and visualize pose trajectories in 3D with orientation indicators.

Quick Start

from pywayne.vio.tools import SE3_to_pose, pose_to_SE3, visualize_pose
import numpy as np

# Convert SE(3) to pose
SE3 = np.eye(4)  # Single 4x4 SE(3) matrix
pose = SE3_to_pose(SE3)  # Returns [tx, ty, tz, qw, qx, qy, qz]

# Batch conversion
SE3_array = np.random.randn(10, 4, 4)  # 10 SE(3) matrices
poses = SE3_to_pose(SE3_array)

# Convert back to SE(3)
SE3_recon = pose_to_SE3(pose)

# Visualize poses
visualize_pose(poses)  # 3D plot with position markers and orientation arrows

Core Functions

SE3_to_pose

Convert SE(3) transformation matrices to pose representation.

Input: SE3_mat - Single 4x4 SE(3) matrix or array of N SE(3) matrices shape (N, 4, 4)

Output: pose - Array shape (7,) or (N, 7) containing [tx, ty, tz, qw, qx, qy, qz]

Dependencies: qmt.quatFromRotMat() for quaternion extraction

pose_to_SE3

Convert pose representation back to SE(3) transformation matrices.

Input: pose_mat - Single pose shape (7,) or array of N poses shape (N, 7) with [tx, ty, tz, qw, qx, qy, qz]

Output: SE3_mat - Array of SE(3) matrices shape (4, 4) or (N, 4, 4)

Dependencies: qmt.quatToRotMat(), ahrs.Quaternion

visualize_pose

3D visualization of SE(3) poses with position markers and orientation arrows.

Parameters:

poses: Array of poses [tx, ty, tz, qw, qx, qy, qz]
arrow_length_ratio: Scale factor for orientation arrows (default: 0.1)

Visualization:

Black dots: Position (translation)
Red arrow: X-axis orientation
Green arrow: Y-axis orientation
Blue arrow: Z-axis orientation

Dependencies: matplotlib.pyplot, qmt.quatToRotMat()

Data Formats

SE(3) Matrix

[[R00 R01 R02 tx]
 [R10 R11 R12 ty]
 [R20 R21 R22 tz]
 [  0   0   0  1]]

Pose Representation

[tx, ty, tz, qw, qx, qy, qz]

Translation: tx, ty, tz (meters) Quaternion: qw, qx, qy, qz (Hamilton convention)

Dependencies

Required packages:

numpy - Array operations
qmt - Quaternion utilities
ahrs - Quaternion class
matplotlib - 3D visualization

Install with:

pip install numpy qmt ahrs matplotlib

---name: pywayne-vio-toolsdescription: VIO (Visual Inertial Odometry) data processing utilities for SE(3) pose conversion and 3D visualization. Use when working with SE(3) matrices and need pose representation conversion (tx,ty,tz,qw,qx,qy,qz), 3D visualization of pose trajectories with orientation arrows, or Visual Inertial Odometry data transformation and analysis--- # Pywayne VIO Tools ## Overview Provides utilities for VIO data processing: convert between SE(3) transformation matrices and pose representations, and visualize pose trajectories in 3D with orientation indicators. ## Quick Start ```python from pywayne.vio.tools import SE3_to_pose, pose_to_SE3, visualize_pose import numpy as np # Convert SE(3) to pose SE3 = np.eye(4) # Single 4x4 SE(3) matrix pose = SE3_to_pose(SE3) # Returns [tx, ty, tz, qw, qx, qy, qz] # Batch conversion SE3_array = np.random.randn(10, 4, 4) # 10 SE(3) matrices poses = SE3_to_pose(SE3_array) # Convert back to SE(3) SE3_recon = pose_to_SE3(pose) # Visualize poses visualize_pose(poses) # 3D plot with position markers and orientation arrows ``` ## Core Functions ### SE3_to_pose Convert SE(3) transformation matrices to pose representation. **Input:** `SE3_mat` - Single 4x4 SE(3) matrix or array of N SE(3) matrices shape (N, 4, 4) **Output:** `pose` - Array shape (7,) or (N, 7) containing [tx, ty, tz, qw, qx, qy, qz] **Dependencies:** `qmt.quatFromRotMat()` for quaternion extraction ### pose_to_SE3 Convert pose representation back to SE(3) transformation matrices. **Input:** `pose_mat` - Single pose shape (7,) or array of N poses shape (N, 7) with [tx, ty, tz, qw, qx, qy, qz] **Output:** `SE3_mat` - Array of SE(3) matrices shape (4, 4) or (N, 4, 4) **Dependencies:** `qmt.quatToRotMat()`, `ahrs.Quaternion` ### visualize_pose 3D visualization of SE(3) poses with position markers and orientation arrows. **Parameters:** - `poses`: Array of poses [tx, ty, tz, qw, qx, qy, qz] - `arrow_length_ratio`: Scale factor for orientation arrows (default: 0.1) **Visualization:** - Black dots: Position (translation) - Red arrow: X-axis orientation - Green arrow: Y-axis orientation - Blue arrow: Z-axis orientation **Dependencies:** `matplotlib.pyplot`, `qmt.quatToRotMat()` ## Data Formats ### SE(3) Matrix ``` [[R00 R01 R02 tx] [R10 R11 R12 ty] [R20 R21 R22 tz] [ 0 0 0 1]] ``` ### Pose Representation ``` [tx, ty, tz, qw, qx, qy, qz] ``` Translation: tx, ty, tz (meters) Quaternion: qw, qx, qy, qz (Hamilton convention) ## Dependencies Required packages: - `numpy` - Array operations - `qmt` - Quaternion utilities - `ahrs` - Quaternion class - `matplotlib` - 3D visualization Install with: ```bash pip install numpy qmt ahrs matplotlib ```

Frequently asked questions