Unscented Kalman Filter Project Starter Code

Self-Driving Car Engineer Nanodegree Program

Overview

In this project we implement an Unscented Kalman Filter in order to predict and model simulated path. The UKF is well suited to handling non-linear models. The CTRV model used in this is non-linear and well suited to testing with the UKF.

Details on the algorithm and functions are available here;

[PDF] (https://pdfs.semanticscholar.org/5dd9/709902c328c8f8cc8aa0d02ce2f23dac41c7.pdf)

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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/sample-laser-radar-measurement-data-1.txt output.txt

Visualizing the results

There is a Jupyter Notebook (python) file in the /results directory that can help visualize the output files from the UnscentedKF program. It provides a visual map and plots the NIS estimates against the 95% percentile

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.