Extended Kalman Filter Project Starter Code

Self-Driving Car Engineer Nanodegree Program

This project is a short demonstration of sensor fusion with simulated radar and lidar data sets to track pedestrian movement. The data is generated from utilities repo and is gaussian in nature (real pedestrians would be much safer if they walked in a gaussian form)

Sensor fusion takes place in a Extended Kalman Filter. The filter models the movement (position and velocity) of pedestrians from the data.

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Building the Project

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
4. Run it: ./ExtendedKF path/to/input.txt path/to/output.txt. You can find some sample inputs in 'data/'.
   • eg. ./ExtendedKF ../data/sample-laser-radar-measurement-data-1.txt output.txt

Generating Additional Data

This is optional!

If you'd like to generate your own radar and lidar data, see the utilities repo for Matlab scripts that can generate additional data.