Sensor Fusion with Extended Kalman Filters

This is the first project in the second term of the Self Driving Car Nanodegree course offered by Udacity.

In this project we fuse the Lidar and Radar measurements to estimate the position of objects on the road such as pedestrains, bicycles, other cars, etc. This project uses Extended Kalman Filters for prediction and update. Here we assume the vehicle is moving with a constant velocity in a straight line. See the next project with Unscented Kalman Filters to handle turns as well.

I implemented this project in C++.

The following gives details on how to run the code:

Dependencies

• cmake >= 3.5
• All OSes: click here for installation instructions
• make >= 4.1
  • Linux: make is installed by default on most Linux distros
  • Mac: install Xcode command line tools to get make
  • Windows: Click here for installation instructions
• gcc/g++ >= 5.4
  • Linux: gcc / g++ is installed by default on most Linux distros
  • Mac: same deal as make - [install Xcode command line tools]((https://developer.apple.com/xcode/features/)
  • Windows: recommend using MinGW

Basic Build Instructions

1. Clone this repo.
2. Make a build directory: mkdir build && cd build
3. Compile: cmake .. && make
   • On windows, you may need to run: cmake .. -G "Unix Makefiles" && make
4. Run it: ./ExtendedKF path/to/input.txt path/to/output.txt