SLAM using Extended Kalman Filter

This project aims to simultaneously localize a robot and map an unknown outdoor environment using IMU data and a 2D stereo camera features. An EKF based approach is taken to achieve the objective.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

Please review requirements.txt

Code organization

.
├── docs                   # Folder contains robot and data specs
├── report                 # Folder contains my report analysis
├── results                # Folder contains final results images
├── src                    # Python scripts
│   ├── main.py            # Main Visual-Intertial SLAM  file
│   ├── slam.py            # Helper for Visual-Intertial SLAM
│   ├── slam_utils.py	   # Utility sets of the of the SLAM
│   └── visualize_utils.py # Utility sets to visualize the result
└── README.md

Running the tests

Steps

1. Modify line 11 in main.py if you want to try different dataset.
2. Run the command python main.py and the resulting images will display.
3. You can change visualization funciton at line 24 to visualize different result, see more details in visualize_utils.py.

Implementations

• See the report for detailed implementations.

Results

Authors

• Arthur Hsieh - Initial work - arthur960304