In this project we utilize a Kalman Filter to estimate the state of a moving object of interest with noisy lidar and radar measurements. A linear kalman filter has been used for working with Lidar measurements and an extended kalman filter has been used to deal with the radar measurments. The estimations of the two filters are combined to yield a complete state estimation system.

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.

The simulator provides us with two datasets. The difference is in the starting measurment: The first starts with a Lidar measurement and the second starts with a Radar measurement.

Use the commands to build the project:

mkdir build && cd build
cmake .. && make

After building the project, run the project:

cd build
./ExtendedKF

• Video for the same

• Youtube video for the same

• The final error for Dataset One are:

RMSE
X: 0.0974
Y: 0.0855
VX: 0.4517
VY: 0.4404

• The final error for Dataset Two are:

RMSE
X: 0.0726
Y: 0.0965
VX: 0.4219
VY: 0.4937