Extended Kalman Filter

This Project is the sixth task (Project 1 of Term 2) of the Udacity Self-Driving Car Nanodegree program. The main goal of the project is to apply Extended 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.

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
  • FusionEKF.cpp - initializes the filter, calls the predict function, calls the update function
  • kalman_filter.cpp- defines the predict function, the update function for lidar, and the update function for radar
  • tools.cpp - a function to calculate RMSE and the Jacobian matrix
• data a directory with two input files, provided by Udacity
• results a directory with output and log files
• Docs a directory with files formats description
• task.md the task of the project by Udacity
• extra a directory with detailed information used hardware and software (extra/additional_info.txt file) and screenshots of the final RMSE.

Result

Accuracy - RMSE: [0.0651648, 0.0605379, 0.533212, 0.544193]

Threshold: RMSE <= [0.08, 0.08, 0.60, 0.60] Accuracy - RMSE: [0.18566, 0.190271, 0.474522, 0.811142]

Threshold: RMSE <= [0.20, 0.20, .50, .85]

The results were visualized with Sensor Fusion utilities.

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

For details, see task.md

The resulted output files are supplied in the results directory.