SLAM using particle filter

This project aims to simultaneously localize a walking humanoid robot and map an unknown indoor environment using odometry data, and a 2D laser range scanner (LIDAR). A particle filter 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

Plese download the data

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 particle filter SLAM file
│   ├── tools.py          # Helper for partical filter SLAM
│   ├── load_data.py	  # Load lidar / joint / cam data
│   ├── map_utils.py	  # Utility sets of the map function
│   └── motion_utils.py	  # Utility sets of the motion function
└── README.md

Running the tests

Steps

1. Modify line 12 and 13 in main.py if you want to try different dataset.
2. Run the command python main.py and the resulting images will display.

Implementations

• See the report for detailed implementations.

Results

Authors

• Arthur Hsieh - Initial work - arthur960304

References

[1] - Sebastian Thrun, “Particle Filters in Robotics"