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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 r...
VIO(视觉惯性里程计)数据处理工具,提供 SE(3)姿态转换及 3D 可视化功能,适用于处理 SE(3) 矩阵并获取姿态信息的场景。
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未分类 clawhub v0.1.2 1 版本 99545.5 Key: 无需
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概述

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

版本历史

共 1 个版本

  • v0.1.2 当前
    2026-05-12 05:21 安全 安全

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