Unscented Kalman Filter

This Project is second project of second term of Udacity Self-Driving Car Nanodegree program. The goal is to apply Unscented Kalman Filter to data from LIDAR and RADAR sensors by C++.

Content

scr contains source code:
- main.cpp - loads data, calls a function to run the Unscented Kalamn Filter(UKF), calls a function to calculate RMSE
- data_source.cpp - load input data into measurement and ground truth vectors
- ukf.cpp - initializes the filter, calls the predict function, calls the update function
- tools.cpp - a function to calculate RMSE and the Jacobian matrix
data a directory with input files, provided by Udacity
result a directory with output files, log files

Results

In order to see how sensor data impact accuracy of Kalman Filter, I ran the algorithm on two data input (porvided by Udacity). Here are the RMSEs comparion of expected and above calculations:

Data 1

RMSE	px	py	vx	vy
Threshold	0.09	0.09	0.65	0.65
Radar + Lidar	0.0772625	0.0817972	0.589278	0.574289
Radar	0.104241	0.111766	0.57746	0.572511
Lidar	0.0924323	0.0831921	0.650915	0.608865

Data 2

RMSE	px	py	vx	vy
Threshold	0.20	0.20	0.55	0.55
Radar + Lidar	0.19322	0.189588	0.300693	0.435957
Radar	0.249102	0.707066	1.63246	0.932646
Laser	0.319107	2.76411	4.85391	5.10034

Lessons Learned

Considering both sensor data (LIDAR and RADAR) give much better accuracy. You can see in RSME table that when we are considering both sensor data, we have better and acceptable RMSE.
Lidar sensor give more accurate measurement than radar
UKF get better accuracy than EKF.

Build Instructions

Clone this repo.
Make a build directory: mkdir build && cd build
Compile: cmake ../src && make
- On windows, you may need to run: cmake ../src -G "Unix Makefiles" && make
Run it: ./UKF path/to/input.txt path/to/output.txt true|false true|false. You can find some sample inputs in 'data/'.
- eg. ./UKF ../data/sample-laser-radar-measurement-data-1.txt output.txt true|false true|false

AliMehrpour / SelfDrivingCar-Term2-P2-UnscentedKalmanFilter