fyng / SLAM

Robot sensing using wheel odometry and 2D LIDAR

Simultaneous Localization and Mapping (SLAM) algorithm for environment mapping using robot wheel odometry and LIDAR data

SLAM corrects for suboptimal robot dimension parameter settings

Occupancy Map - Odometry only, width = 500 mm	Path - Odometry only, width = 500 mm	Occupancy Map - SLAM, width = 500 mm	Path - SLAM, width = 500 mm

SLAM further refines robot trajectory and environment map when the robot dimension parameter is close to optimal

Occupancy Map - Odometry only, width = 730 mm	Path - Odometry only, width = 730 mm	Occupancy Map - SLAM, width = 730 mm	Path - SLAM, width = 730 mm

Usage

Install micromamba or mamba as the package manager. To install micromamba, refer to the installation guide

To install the classifier:

1. Clone the repo

git clone https://github.com/fyng/SLAM.git

cd SLAM

2. Create virtual environment

micromamba env create -f environment.yml

micromamba activate slam

3. Create directory for data and plots

4. Run model In main.py, update the directory of the test folder. Chnage the car variables as needed

python main.py

Acknowledgement

ECE 5242 - Intelligent Autonomous Systems taught by Prof Daniel Lee & Travers Rhodes