SE(3) Manifold Optimization & Benchmarks

This repository contains a collection of algorithms and benchmarks for optimization on the SE(3) manifold, focusing on Geometric Dual Quaternions (GeoDQ), and other geometric fusion methods.

Designed for Computer Vision, Robotics, and Navigation tasks.

📚 Algorithms Collection

Project / Algorithm	Description	Code & Benchmarks	Documentation
GeoDQ-Bench	Geometric State Fusion using Dual Quaternion Observer. Comparative analysis with Kalman Filters (ESKF, UKFM).	Code Benchmark	Read details
ESKF / UKFM	Error-State and Unscented Kalman Filter implementations for SE(3) navigation tasks.	Code	Read details
TBD	TBD	Coming soon	Coming soon

🛠️ Setup & Usage

Installation & Environments

This repository supports two workflows requiring different environments.

1. Navigation Algorithms (ESKF, GeoDQ)

Standard environment for manifold optimization algorithms.

git clone https://github.com/afanasyspb/SE3-Manifold-Lib.git
cd SE3-Manifold-Lib
pip install -r requirements.txt

2. Point Cloud Processing & CGA (LiDAR, SLAM)

Requires Python 3.10 due to Open3D compatibility. Please use the provided Conda environment.

# Create the environment from file
conda env create -f environment_cga.yml

# Activate
conda activate kitti_cga_env