The robot has been kidnapped and transported to a new location! Luckily it has a map of this location, a (noisy) GPS estimate of its initial location, and lots of (noisy) sensor and control data.
In this project we implement a 2 dimensional particle filter in C++. The particle filter is given a map and some initial localization information (analogous to what a GPS would provide). At each time step the filter also gets observation and control data.
The particle filter has to localize the robot as it moves across the map.
The project has been developed on a Linux machine with Python 3.6. The system was provided by Udacity for this particular project.
Following are the dependencies:
- cmake >= 3.5
- make >= 4.1
- gcc/g++ >= 5.4
- Udacity's Term 2 Simulator. Link
To install the dependencies, use the script install-linux.sh
Synthetic data provided by Udacity is used for the project. The data is present in the data directory. It consists of measurements in a txt file format.
Use the commands to build the project:
./clean.sh
./build.shAfter building the project, run the project:
./run.shRMSE
x: 0.115
y: 0.110
yaw: 0.004