Unscented Kalman Filter

This Project is the seventh task (Project 2 of Term 2) of the Udacity Self-Driving Car Nanodegree program. The main goal of the project is to apply Unscented Kalman Filter to fuse data from LIDAR and Radar sensors of a self driving car using C++.

The project was created with the Udacity Starter Code.

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Content of this repo

• scr a directory with the project code:
  • main.cpp - reads in data, calls a function to run the Kalman filter, calls a function to calculate RMSE
  • ukf.cpp - the UKF filter itself, defines the predict function, the update function for lidar, and the update function for radar
  • tools.cpp - a function to calculate RMSE
• data a directory with two input files, provided by Udacity
• results a directory with output files

Results

1. Input file: sample-laser-radar-measurement-data-1.txt

RMSE = [0.0758215, 0.0842188, 0.632344, 0.580668]

Threshold: RMSE <= [0.09, 0.09, 0.65, 0.65]

2. Input file: sample-laser-radar-measurement-data-2.txt

RMSE = [0.194559 0.189894 0.518945 0.507547]

Threshold: RMSE <= [0.20, 0.20, 0.55, 0.55]

Dependencies

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

How to run the code

Clone this repo and perform

mkdir build && cd build
cmake .. && make
./ExtendedKF ../data/sample-laser-radar-measurement-data-1.txt output1.txt > input1.log
./ExtendedKF ../data/sample-laser-radar-measurement-data-2.txt output2.txt > input2.log