https://github.com/NikolasEnt/Extended-Kalman-Filter

Udacity Self-Driving Car Engineer Nanodegree. Project: Extended Kalman Filters
https://github.com/NikolasEnt/Extended-Kalman-Filter

carnd kalman-filter self-driving-car sensor-fusion

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Udacity Self-Driving Car Engineer Nanodegree. Project: Extended Kalman Filters

• Host: GitHub
• URL: https://github.com/NikolasEnt/Extended-Kalman-Filter
• Owner: NikolasEnt
• Created: 2017-03-30T23:39:40.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-04-15T23:08:49.000Z (over 7 years ago)
• Last Synced: 2024-04-09T03:14:24.757Z (6 months ago)
• Topics: carnd, kalman-filter, self-driving-car, sensor-fusion
• Language: C++
• Homepage:
• Size: 1.17 MB
• Stars: 28
• Watchers: 4
• Forks: 27
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# 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](https://github.com/udacity/CarND-Extended-Kalman-Filter-Project).

## 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](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

![input 1 results](readme_img/plot1.png)

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

*Threshold*: RMSE <= [0.08, 0.08, 0.60, 0.60]

![input 2 results](readme_img/plot2.png)

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](https://github.com/udacity/CarND-Mercedes-SF-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](task.md)

The resulted output files are supplied in the [results](results) directory.