This repository contains all the code needed to complete the final project for the Localization course in Udacity's Self-Driving Car Nanodegree.
All you will submit is your completed version of
particle_filter.cpp, which is located in the
src directory. You should probably do a
git pull before submitting to verify that your project passes the most up-to-date version of the grading code (there are some parameters in
src/main.cpp which govern the requirements on accuracy and run time.)
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
// get the previous velocity and yaw rate to predict the particle's transitioned state
// receive noisy observation data from the simulator, in a respective list of x/y values
OUTPUT: values provided by the c++ program to the simulator
// best particle values used for calculating the error evaluation
//Optional message data used for debugging particle's sensing and associations
// for respective (x,y) sensed positions ID label
// 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
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.
Inputs to the Particle Filter
You can find the inputs to the particle filter in the
map_data.txt includes the position of landmarks (in meters) on an arbitrary Cartesian coordinate system. Each row has three columns
- x position
- y position
- landmark id
All other data the simulator provides, such as observations and controls.
- 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:
Accuracy: your particle filter should localize vehicle position and yaw to within the values specified in the parameters
Performance: your particle filter should complete execution within the time of 100 seconds.