State Estimation using Dual Quaternions

This repository contains the code for a robotics project that implements state estimation using Dual Quaternions. The approach fuses wheel encoder and IMU sensor data to estimate the robot's pose, velocity, and covariance using a dual quaternion-based predict-correct filter.

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Project Summary

• Translation data from wheel encoders
• Orientation data from IMU
• Dual quaternion operations for pose and velocity propagation
• Custom Kalman filter-like state estimation using predict() and correct() functions

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Repository Structure

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├── `include/`                 # Header for dual quaternion operations  
├── `src/`                     # Main localization logic, callbacks, predict/correct functions  
├── `launch/`                  # ROS launch file for bringing up the node  
├── `urdf/`                    # URDF model of the robot  
├── `meshes/`                  # 3D model resources used in URDF  
├── `worlds/`                  # Gazebo simulation world  
├── `rviz/`                    # RViz configuration for visualization
├── `param/`                   # Parameter files for ROS nodes
├── dqekf_paper_draft.pdf      # Draft paper describing methodology and implementation
├── README.md                  # Project overview

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Project Paper Draft

For a detailed explanation of the methodology, dual quaternion math, and results: Draft Paper (PDF)

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Author

Rishab Agrawal