ser94mor / unscented-kalman-filter Goto Github PK

In this project, an unscented Kalman filter is utilized to estimate the state of a moving object of interest with noisy lidar and radar measurements.

This project involves the Udacity Self-Driving Car Engineer Nanodegree Term 2 Simulator which can be downloaded from here.

• cmake >= 3.5
• make >= 4.1 (Linux, Mac), 3.81 (Windows)
• gcc/g++ >= 5.4
• uWebSocketIO with commit hash e94b6e1

The Unscented Kalman Filter program can be built and run by doing the following from the project top directory.

$> mkdir build
$> cd build
$> cmake ..
$> make

Run from the project top directory.

$> build/UnscentedKF

More statistics can be found in NIS_Analysis.ipynb.

Below 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 measurement 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"]

For comprehensive instructions on how to install and run project, please, refer to the following repo, which was used as a skeleton for this project: https://github.com/udacity/CarND-Unscented-Kalman-Filter-Project.