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Extended Kalman Filter

Intro

In this project utilize a Kalman filter to estimate the state of a moving object of interest with noisy Lidar and Radar measurements.

This project involves Udacity Term 2 Simulator which can be downloaded here

This repository includes two files that can be used to set up and intall uWebSocketIO for either Linux or Mac systems. For Windows you can use either Docker, VMware, or even Windows 10 Bash on Ubuntu to install uWebSocketIO.

Once the install for uWebSocketIO is complete, the main program can be built and ran by doing the following from the project top directory.

  1. mkdir build
  2. cd build
  3. cmake ..
  4. make
  5. ./ExtendedKF

Note that the programs that need to be written to accomplish the project are src/FusionEKF.cpp, src/FusionEKF.h, kalman_filter.cpp, kalman_fitler.h, tools.cpp, and tools.h

Sensor Fusion General Processing Flow

alt text

Here is the main protcol that main.cpp uses for uWebSocketIO in communicating with the simulator.

INPUT: values provided by the simulator to the c++ program.

["sensor_measurement"] => the measurment that the simulator observed (either lidar or radar).

OUTPUT: values provided by the c++ program to the simulator.

["estimate_x"] <= kalman filter estimated position x ["estimate_y"] <= kalman filter estimated position y ["rmse_x"] ["rmse_y"] ["rmse_vx"] ["rmse_vy"]


Other Important Dependencies

Basic Build Instructions

  1. Clone this repo.
  2. Make a build directory: mkdir build && cd build
  3. Compile: cmake .. && make
    • On windows, you may need to run: cmake .. -G "Unix Makefiles" && make
  4. Run it: ./ExtendedKF path/to/input.txt path/to/output.txt. You can find some sample inputs in 'data/'.
    • eg. ./ExtendedKF ../data/obj_pose-laser-radar-synthetic-input.txt

Generating Additional Data

To generate your own radar and lidar data, see the utilities repo for Matlab scripts that can generate additional data.

Final result with dataset 1:

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Final result with dataset 2:

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