Unscented Kalman Filter Project

In this project utilize an Unscented Kalman Filter to estimate the state of a moving object of interest with noisy lidar and radar measurements.

The CTRV (Constant Turn Rate and Velocity magnitude) model was used.

This project involves the Term 2 Simulator which can be downloaded HERE

This repository includes two files that can be used to set up and intall uWebSocketIO for either Linux or Mac systems. For windows you can use either Docker, VMware, or even Windows 10 Bash on Ubuntu to install uWebSocketIO.

Once the install for uWebSocketIO is complete, the main program can be built and ran by doing the following from the project top directory.

1. mkdir build
2. cd build
3. cmake ..
4. make
5. ./UnscentedKF

Here is the main protcol that main.cpp uses for uWebSocketIO in communicating with the simulator.

INPUT: values provided by the simulator to the c++ program

• ["sensor_measurement"] => the measurment that the simulator observed (either lidar or radar)

OUTPUT: values provided by the c++ program to the simulator

• ["estimate_x"] <= kalman filter estimated position x
• ["estimate_y"] <= kalman filter estimated position y
• ["rmse_x"]
• ["rmse_y"]
• ["rmse_vx"]
• ["rmse_vy"]

Other Important Dependencies

• cmake >= v3.5
• make >= v4.1
• gcc/g++ >= v5.4

Basic Build Instructions

1. Clone this repo.
2. Make a build directory: mkdir build && cd build
3. Compile: cmake .. && make
4. Run it: ./UnscentedKF path/to/input.txt path/to/output.txt. You can find some sample inputs in 'data/'.
   • eg. ./UnscentedKF ../data/obj_pose-laser-radar-synthetic-input.txt

Generating Additional Data

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