Kidnapped vehicle localization using Particle filter

A vehicle 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. In this project , 2 dimensional particle filter is implemented in C++ to localize the vehicle in the map. The particle filter will be given a map and some initial localization information (analogous to what a GPS would provide). At each time step the filter will also get observation and control data.

This project is done as part of the self-driving car nanodegree .

---

Dependencies

• cmake >= 3.5
  • All OSes: click here for installation instructions
• make >= 4.1 (Linux, Mac), 3.81 (Windows)
  • Linux: make is installed by default on most Linux distros
  • Mac: install Xcode command line tools to get make
  • Windows: Click here for installation instructions
• gcc/g++ >= 5.4
  • Linux: gcc / g++ is installed by default on most Linux distros
  • Mac: same deal as make - install Xcode command line tools
  • Windows: recommend using MinGW
• uWebSocketIO
  • This package facilitates the connection between the simulator and code by setting up a web socket server connection from the C++ program to the simulator, which acts as the host.
  • To install it on linux , run the script install-ubuntu.sh .
  • To install it on mac , run the script install-mac.sh .
• This project involves the Term 2 Simulator which can be downloaded here

---

Running the Code

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

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

// 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

To complete the project , build out the methods in particle_filter.cpp until the simulator output says:

Success! Your particle filter passed!

---

Implementing the Particle Filter

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

• src/particle_filter.h contains the declaration of ParticleFilter class.
• src/particle_filter.cpp contains the implementation of ParticleFilter class.
• src/main.cpp contains the code that will actually be running your particle filter , calling the associated methods and communicationg with the simulator.

---

Inputs to the Particle Filter

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

The Map

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

All other data the simulator provides, such as observations and controls.

• Map data provided by 3D Mapping Solutions GmbH.

---

Success Criteria

If the particle filter passes the current grading code in the simulator , the simulator output says:

Success! Your particle filter passed!

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.

---

Results

The simulator is able to track the car to a particle as the green laser sensors from the car nearly overlap the blue laser sensors from the particle, this means that the particle transition calculations were done correctly.

---