README.md

Extended Kalman Filter

In this project, we use an extended kalman filter to predict the location and velocity of a simulated bicycle that is traveling around the vehicle. The measurement data comes from lidar and radar sensors with the main algorithm implemented in C++.

Example of Predicted location

In the screenshots below, the green triangles represent the predicted location, the red circles are from the laser sensor, and the blue markers are from the radar sensor. We measure the accuracy of the algorithm by calculating the RMSE of the x, y positions and the velocity along the x, y axis.

• The original dataset starting with lidar measurement

• Reverse of the original dataset starting with radar measurement

If we just use one or the other of the sensor measurements to update the algorithm we can start to see what each sensor is better.

• The original dataset starting with lidar measurement and only using the lidar measurements to update to algorithm. We can see that compared to using both sources of sensor data the overall algorithm performs worse.

• The original dataset starting with lidar measurement and only using the radar measurements to update to algorithm. Compared to using only the lidar data, the radar only updated algorithm is worse at localizing the positon (higher RMSE for x and y).

Compile and Build

In order to compile and build this project, make sure that the following dependencies are met.

• cmake:
  • For Mac make sure that cmake is at least version 3.5
• make:
  • For Mac make sure that make is at least version 4.1
• gcc/g++:
  • For Mac make sure that gcc/g++ is at least version 5.4
• uWebSocketIO
  • From the project directory run install-mac.sh, which should be linked to the necessary cmakepatch.txt file
  • In order to run the above shell script, homebrew should also be installed

Once the above dependencies are installed:

1. Clone this repository
2. Create a build directory and navigate into it

• mkdir build && cd build

3. Compile

• cmake .. && make

4. Run the program

• Make sure that the Udacity simulator is installed
• ./ExtendedKF