This is my Project 3 in term 2 of the Ucatity Self-Driving Car Engineer Nanodegree Program, which aims to utilize the final particle filter project for the Localization course to estimate the state of a kidnapped, moving vehicle based on a CTRV model, with noisy measurements using C++. In this project I chose Ubuntu 14.04 LTS for implementation.
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 sensor observation(p_x,p_y,p_theta) and control data(velocity and yaw_rate).
This project origins from the Udacity Starter Code repository,which includes uWebSocketIO installation files install-ubuntu.sh and 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
Data: 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.
Note the only file I should modify is particle_filter.cpp in the src directory, or basic requirements in project rubrics. And the main protcol that main.cpp uses is the web socket server uWebSocketIO acting as a host, connecting the C++ programs to the Unity simulator, where the input values is provided by the simulator to the c++ program, and output values provided by the c++ program to the simulator. 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. And I did take a look at src/main.cpp as well, which contains the code that will actually be running your particle filter and calling the associated methods.
The flow of Particle Filter is Initialization -> Prediction -> Update ( update weight with data association) -> Resampling on this synthetic dataset, so I can narrow down the source of error when run into some problems.
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
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:
- ./clean.sh
- ./build.sh
- ./run.sh
Tips for setting up your environment can be found here
Note that the programs that need to be written to accomplish the project are src/particle_filter.cpp. The program main.cpp has already been filled out, but feel free to modify it.
INPUT: values provided by the simulator to the c++ program
You can find the inputs to the particle filter in the data directory.
-
// 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
My job is to build out the methods in particle_filter.cpp until the simulator output says:
Success! Your particle filter passed!
![alt text] image1
Here I stick to Google's C++ style guide. And I use the ubuntu file editor gedit with the similar user interface with emacs.
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.)
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
max_translation_errorandmax_yaw_errorinsrc/main.cpp. -
Performance: your particle filter should complete execution within the time of 100 seconds.
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