/Particle-Filter

Car localization for Udacity's Self-driving Car Engineering Nanodegree

Primary LanguageC++MIT LicenseMIT

Overview

This repository contains my code for the final project of the localization portion in Udacity's Self-Driving Car Nanodegree. The code implements a two-dimensional particle filter.

Project Introduction

In this project, the robot has been kidnapped and transported to a new location. Unlike a full simultaneous localization and mapping (SLAM) approach, the robot has a map of the landmarks of the location, a (noisy) GPS estimate of its initial location, and lots of (noisy) sensor and control data.

The two-dimensional particle filter is implemented in C++. At each time step the particle filter will receive an observation and control data.

Running the Code

This project involves Udacity's 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 run 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 the environment can be found here

Use of uWebSocketID

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

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

Inputs to the Particle Filter

The inputs to the particle filter are 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.

  1. Accuracy: The 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: The particle filter should complete execution within the time of 100 seconds.