Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Pywayne Vio Se3
SE(3) rigid body transformation library for 3D rotation and translation operations. Use when working with robot poses, camera transformations, SLAM systems,...
skill openclawclawhub Free
SE(3) rigid body transformation library for 3D rotation and translation operations. Use when working with robot poses, camera transformations, SLAM systems,...
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- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- wangyendt
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Documentation
Quick Start
import numpy as np from pywayne.vio.SE3 import * # Create SE(3) transformation from rotation and translation R = np.eye(3) t = np.array([1, 2, 3]) T = SE3_from_Rt(R, t) # Lie algebra operations xi = np.array([0.1, 0.2, 0.3, 0.05, 0.1, 0.15]) # [rho, theta] T_from_xi = SE3_Exp(xi) # se(3) vector -> SE(3) xi_recovered = SE3_Log(T_from_xi) # SE(3) -> se(3) vector
Basic Matrix Operations
Create/Verify SE(3) matrices: check_SE3(T) - Validate 4x4 matrix is valid SE(3) SE3_from_Rt(R, t) - Construct from rotation matrix and translation SE3_to_Rt(T) - Extract rotation matrix and translation vector Combine/invert transformations: SE3_mul(T1, T2) - Matrix multiplication (compose transforms) SE3_inv(T) - Matrix inverse SE3_diff(T1, T2, from_1_to_2=True) - Compute relative transform
Lie Group/Lie Algebra Mappings
Vector form (preferred): SE3_Exp(xi) - se(3) 6D vector -> SE(3) matrix, xi = [rho, theta] SE3_Log(T) - SE(3) matrix -> se(3) 6D vector Matrix form (theoretical): SE3_exp(xi_hat) - se(3) 4x4 matrix -> SE(3) matrix SE3_log(T) - SE(3) matrix -> se(3) 4x4 matrix SE3_skew(xi) - 6D vector -> 4x4 Lie algebra matrix SE3_unskew(xi_hat) - 4x4 matrix -> 6D vector Naming convention: Uppercase = vector, lowercase = matrix
Representation Conversions
Quaternion + translation: SE3_from_quat_trans(q, t) - q is wxyz quaternion SE3_to_quat_trans(T) - Returns (quaternion, translation) Axis-angle + translation: SE3_from_axis_angle_trans(axis, angle, t) SE3_to_axis_angle_trans(T) - Returns (axis, angle, translation) Euler angles + translation: SE3_from_euler_trans(euler_angles, t, axes='zyx', intrinsic=True) SE3_to_euler_trans(T, axes='zyx', intrinsic=True)
Statistical Operations
SE3_mean(T_batch) - Compute mean of multiple SE(3) matrices (Nx4x4 -> 4x4)
Input/Output Formats
Single transformation: Input: 4x4 or 3x3/3 arrays Output: 4x4 or scalar vectors Batch operations: Input: Nx4x4 or Nx3x3/Nx3 arrays Output: Same batched format All functions support both single and batch inputs 6D vector format: [rho_1, rho_2, rho_3, theta_1, theta_2, theta_3] First 3: translation (linear velocity) Last 3: rotation (angular velocity)
Trajectory Processing
# Batch process robot trajectory poses = np.array([...]) # Nx4x4 log_poses = SE3_Log(poses) # Nx6 Lie algebra space mean_pose = SE3_Exp(np.mean(log_poses, axis=0)) # Intrinsic mean
Relative Motion
# Relative transform between two poses T_rel = SE3_diff(T_world_keyframe1, T_world_keyframe2) # T_rel transforms points from frame2 to frame1
Camera Pose Estimation
# Camera to world transformation R_cam = np.column_stack([right, up, forward]) # Camera axes t_cam = camera_position T_cam2world = SE3_from_Rt(R_cam, t_cam) T_world2cam = SE3_inv(T_cam2world)
Notes
All angles in radians Right-multiply convention: P' = T @ P Numerically stable for large angles and displacements Batch operations use vectorized NumPy for efficiency Performance reference (1000 transforms): Exp ~2.5ms, Log ~0.8ms
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md