Extended Kalman Filter:

Using lidar and radar to track position and velocity

-Lidar: red.

-Radar: blue.

-Predicted locations: green.

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Overview

1. About this simulator
2. Dependencies
3. Build instructions

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About

1. main.cpp reads in lidar and radar readings

2. FusionEKF initializes state variables, and runs prediction / update cycles using kalman_filter.cpp

3. 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

4. tools.cpp calculates the jacobian matrix and the root mean squared error, a measure of distance from ground truth

5. kalman-tracker.py visualizes readings and predictions in a simulator (pictured)

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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

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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. You can find some sample inputs in 'data/'.
   • eg. ./ExtendedKF ../data/obj_pose-laser-radar-synthetic-input.txt