vensonchancmf / kidnapped-vehicle-project Goto Github PK

Project Member: Cuiqing Li

Time: July 2017

The basic idea of this project is that we need to implement particle filter so that we can identify cars' exact locations based on Lidar/Radar measurements and GPS information.

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

Here is the algorithm for patticle filter in this project:

Here is the general view of the whole algorithm:

We also need to use Markov assuption to predict and simulate vehicle movements, and here is a picture displaying how it looks like:

I still need to use Bayes Inference to caluculte the posterior of next position of the car, here is formula about how to handle the problem:

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!

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.