bluerov-kalmanfilter

A comprehensive solution for underwater navigation using BlueROV2.

This repository processes DVL (Doppler Velocity Logs) sensor data to accurately determine the robot's position via a Kalman Filter.

We subscribe to DVL sensor data and publish navigation information in ROS messages of type nav_msgs/Odometry.msg.

---

I. Introduction to Navigation

For more insights into subsea navigation, visit: Nortek Subsea Navigation

II. Introduction to Kalman Filter

1. Understand the theory behind the Kalman Filter:

   • Comprehensive guide: Kalman Filter

2. Learn the Python implementation of the Kalman Filter:

   • Documentation: FilterPy

III. How to Install ROS Noetic

Follow the detailed instructions for ROS Noetic installation on Ubuntu: ROS Noetic Installation

IV. How to Create a ROS Workspace and Package

1. Set up a ROS workspace:

   $ mkdir -p ~/catkin_ws/src
   $ cd ~/catkin_ws/
   $ catkin_make
   $ source devel/setup.bash
   

2. Navigate to your ROS workspace:

   $ cd ~/catkin_ws/src
   

3. Clone this ROS package:

   $ git clone 
   

4. Configure your ROS package:

   $ cd ~/catkin_ws
   $ catkin_make
   $ source devel/setup.bash (Run this command in every shell session used for ROS)
   

---

About the Project

This project aims to enhance the navigation capabilities of underwater robotics, specifically the BlueROV2. By integrating sensor data from DVL (Doppler Velocity Logs) and utilizing a Kalman Filter, it ensures precise position estimation and navigation support for the robot. The Kalman Filter implementation provides robust data fusion and state estimation, crucial for reliable underwater navigation.

Module Name: CA-RIS-801 | Marine Robotics (Spring 2024), Constructor University Bremen