ROS 2 Mapping with SLAM

Objective

This assignment focuses on the practical application of SLAM (Simultaneous Localization and Mapping) in ROS 2. You will create a 2D LIDAR-based map, explore the necessary inputs and outputs for 2D and 3D mapping tools, and explain the mapping algorithms in your own words.

Tasks

Task 1: Create a 2D LIDAR-Based Map

Objective: Using the knowledge gained from the lectures, manually create a maze in Gazebo and generate a 2D map using a LIDAR sensor and the SLAM toolbox.

1. Set Up the Maze Environment:
2. Manually create a maze in Gazebo using the available tools.
3. Ensure that the maze is complex enough to demonstrate the capabilities of the SLAM algorithm.
4. Perform 2D Mapping:
5. Use the SLAM toolbox to generate a 2D map of the maze using TurtleBot3 equipped with a LIDAR sensor.
6. Save the generated map and visualize it in RViz.
7. Document the Process:
8. Provide a step-by-step explanation of how you set up the maze, configured the SLAM toolbox, and generated the map.

Task 2: Understand Inputs and Outputs for 2D and 3D Mapping

Objective: Explore and document the necessary inputs, outputs, and frames required for both 2D and 3D mapping using the SLAM toolbox and the RTAB-Map package.

1. 2D Mapping with SLAM Toolbox:
2. Identify and document the required inputs (e.g., LIDAR data, odometry) and outputs (e.g., map data, tf frames).
3. Explain the role of each input and output in the mapping process.
4. 3D Mapping with RTAB-Map:
5. Identify and document the required inputs (e.g., RGB-D camera data, odometry) and outputs (e.g., point clouds, 3D map data, tf frames).
6. Explain how the inputs are processed and how the outputs are generated.
7. Compare 2D and 3D Mapping:
8. Provide a comparison between 2D and 3D mapping in terms of complexity, accuracy, and the type of environments each is best suited for -> in a documented form

Task 3: Explain the Mapping Algorithm (Gmapping)

Objective: Explain in simple terms how the Gmapping algorithm works for creating maps.

1. Simplified Explanation:
2. Write a brief explanation of the Gmapping algorithm, focusing on the key concepts such as particle filters, map updating, and handling sensor noise.
3. Relate to Practical Application:
4. Relate your explanation to the practical steps you took in Task 1 to create the 2D LIDAR-based map.
5. Highlight how Gmapping contributes to building an accurate and reliable map.

Submission Process

1. Create Files:
2. Navigate to the module_7_assignment package.
3. Create the required files for the maze setup, SLAM configuration, and documentation.
4. Document Your Work:
5. Create a README.md file in the module_7_assignment package.
6. Provide details about the files you created, including explanations of the setup process, inputs/outputs for SLAM, and the Gmapping algorithm.
7. Submit Your Assignment:
8. Push your changes to your forked repository.
9. Provide your repository link in the assignment submission text area.
10. Note: Ensure you press the “Start Assignment” button when you see the page (as it takes time to generate the pages).
11. Wait for Review:
12. Wait for the instructors to review your submission.

Learning Outcome

By completing this assignment, you will:

• Gain hands-on experience with creating maps using 2D LIDAR and 3D RGB-D sensors.
• Understand the necessary inputs and outputs for successful mapping in both 2D and 3D environments.
• Develop the ability to explain mapping algorithms like Gmapping in simple terms and relate them to practical applications.