amit-jain / carnd-kidnapped-vehicle-project

Overview

This repository contains all the code needed to complete the final project for the Localization course in Udacity's Self-Driving Car Nanodegree.

Project Introduction

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.

In this project you will implement a 2 dimensional particle filter in C++. Your particle filter will be given a map and some initial localization information (analogous to what a GPS would provide). At each time step your filter will also get observation and control data.

Running the Code

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 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.

mkdir build cd build cmake .. make ./particle_filter

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 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

Your job is to 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

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.

Implementation

The following number of particles were experimented with while running the program with the simulator:

10

x = 0.162 y = 0.150 yaw = 0.005 runtime = 69.92 seconds

50

x = 0.122 y = 0.112 yaw = 0.004 runtime = 78.42 seconds

100

x = 0.116 y = 0.108 yaw = 0.004 runtime = 80.55 seconds

150

x = 0.110 y = 0.105 yaw = 0.004 runtime = 83.54 seconds

200

x = 0.112 y = 0.107 yaw = 0.004 runtime = 85.80 seconds

The final selection was 100 because over that there was not too much to gain in the error reduction and also to have a reasonable runtime.