The above GIF shows the position of a vehicle, a set of known landmarks on a map as well as the position and orientation of vehicle determined by the particle filter. Not all landmarks measured but only those that fall within the range.

Modified files for this project helper_functions.h main.cpp particle_filter.cpp particle_filter.h,

Your robot has been kidnapped and transported to a new location! Luckily it has a map of this location, a (noisy) GPS estimate of its initial location, and lots of (noisy) sensor and control data.

Like Kalman Filters, particle filter is great way to track the state of dynamic system for which we have the lot of noisy sensor and control data. Particle filtering uses a genetic mutation-selection sampling approach, with a set of particles (also called samples) to represent the posterior distribution of some stochastic process given noisy and/or partial observations. https://en.wikipedia.org/wiki/Particle_filter

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

This repository includes two files that can be used to set up and install 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. ./particle_filter

Alternatively some scripts have been included to streamline this process, these can be leveraged by executing the following in the top directory of the project:

1. ./clean.sh
2. ./build.sh
3. ./run.sh

Tips for setting up your environment can be found here

Note that the programs that need to be written to accomplish the project are src/particle_filter.cpp, and particle_filter.h

The program main.cpp has already been filled out, but feel free to modify it.

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

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

// sense noisy position data from the simulator

["sense_x"]

["sense_y"]

["sense_theta"]

// get the previous velocity and yaw rate to predict the particle's transitioned state

["previous_velocity"]

["previous_yawrate"]

// receive noisy observation data from the simulator, in a respective list of x/y values

["sense_observations_x"]

["sense_observations_y"]

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

// best particle values used for calculating the error evaluation

["best_particle_x"]

["best_particle_y"]

["best_particle_theta"]

//Optional message data used for debugging particle's sensing and associations

// for respective (x,y) sensed positions ID label

["best_particle_associations"]

// for respective (x,y) sensed positions

["best_particle_sense_x"] <= list of sensed x positions

["best_particle_sense_y"] <= list of sensed y positions

Your job is to build out the methods in particle_filter.cpp until the simulator output says:

Success! Your particle filter passed!

The directory structure of this repository is as follows:

root
|   build.sh
|   clean.sh
|   CMakeLists.txt
|   README.md
|   run.sh
|
|___data
|   |   
|   |   map_data.txt
|   
|   
|___src
    |   helper_functions.h
    |   main.cpp
    |   map.h
    |   particle_filter.cpp
    |   particle_filter.h

The only file you should modify is particle_filter.cpp in the src directory. The file contains the scaffolding of a ParticleFilter class and some associated methods. Read through the code, the comments, and the header file particle_filter.h to get a sense for what this code is expected to do.

If you are interested, take a look at src/main.cpp as well. This file contains the code that will actually be running your particle filter and calling the associated methods.

You can find the inputs to the particle filter in the data directory.

map_data.txt includes the position of landmarks (in meters) on an arbitrary Cartesian coordinate system. Each row has three columns

1. x position
2. y position
3. landmark id

• Map data provided by 3D Mapping Solutions GmbH.

If your particle filter passes the current grading code in the simulator (you can make sure you have the current version at any time by doing a git pull), then you should pass!

The things the grading code is looking for are:

1. Accuracy: your particle filter should localize vehicle position and yaw to within the values specified in the parameters max_translation_error and max_yaw_error in src/main.cpp.

2. Performance: your particle filter should complete execution within the time of 100 seconds.