Extended Kalman Filter:

Using lidar and radar to track position and velocity

-Lidar: red.

-Radar: blue.

-Predicted locations: green.

Overview

main.cpp reads in lidar and radar readings
FusionEKF initializes state variables, and runs prediction / update cycles using kalman_filter.cpp
kalman_filter.cpp implements:

-Predict(): calculating new x and P after some time

-Update(): using incoming lidar readings and measurment transition matrix to calculate new x and P

-UpdateEKF(): using linear approximation of lidar readings through Jacobian matrix to calculate new x and P
tools.cpp calculates the jacobian matrix and the root mean squared error, a measure of distance from ground truth
kalman-tracker.py visualizes readings and predictions in a simulator (pictured)

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

Clone this repo.
Make a build directory: mkdir build && cd build
Compile: cmake .. && make
- On windows, you may need to run: cmake .. -G "Unix Makefiles" && make
Run it: ./ExtendedKF path/to/input.txt path/to/output.txt. You can find some sample inputs in 'data/'.
- eg. ./ExtendedKF ../data/obj_pose-laser-radar-synthetic-input.txt