Self-Driving Car Engineer Nanodegree Program
In this project you will utilize a kalman filter to estimate the state of a moving object of interest with noisy lidar and radar measurements. Passing the project requires obtaining RMSE values that are lower than the tolerance outlined in the project rubric.
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 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. Please see the uWebSocketIO Starter Guide page in the classroom within the EKF Project lesson for the required version and installation scripts.
Once the install for uWebSocketIO is complete, the main program can be built and run by doing the following from the project top directory.
- mkdir build
- cd build
- cmake ..
- make
- ./ExtendedKF
Tips for setting up your environment can be found in the classroom lesson for this project.
Note that the programs that need to be written to accomplish the project are src/FusionEKF.cpp, src/FusionEKF.h, kalman_filter.cpp, kalman_filter.h, tools.cpp, and tools.h
The program main.cpp has already been filled out, but feel free to modify it.
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
["sensor_measurement"] => the measurement that the simulator observed (either lidar or radar)
OUTPUT: values provided by the c++ program to the simulator
["estimate_x"] <= kalman filter estimated position x ["estimate_y"] <= kalman filter estimated position y ["rmse_x"] ["rmse_y"] ["rmse_vx"] ["rmse_vy"]
-
cmake >= 3.5
- All OSes: click here for installation instructions
-
make >= 4.1 (Linux, Mac), 3.81 (Windows)
- Linux: make is installed by default on most Linux distros
- Mac: install Xcode command line tools to get make
- Windows: Click here for installation instructions
-
gcc/g++ >= 5.4
- Linux: gcc / g++ is installed by default on most Linux distros
- Mac: same deal as make - install Xcode command line tools
- Windows: recommend using MinGW
Dependencies Keep in mind that the minimum project dependency versions are:
cmake: 3.5 All OSes: click here for installation instructions make: 4.1 Linux: make is installed by default on most Linux distros Mac: install Xcode command line tools to get make Windows: Click here for installation instructions gcc/g++: 5.4 Linux: gcc / g++ is installed by default on most Linux distros Mac: same deal as make - install Xcode command line tools Windows: recommend using MinGW "Hmmm, I don't know what any of those dependencies are..." Fair point! You've been writing C++ in the classroom up to this point. Compiling on your own machine is a different challenge altogether. Let's talk about compiling.
Compiling Compiling is the process of translating the code that you've written into machine code that processors understand. Every program, regardless of the source language, needs to be compiled in order to execute. This is true even for scripting languages like Python or JavaScript. In these cases, the interpreter (or a similar system) is responsible for compiling code on the fly in a process known as just-in-time compiling. To the user, compiling and execution are effectively a single action. (Of course, the actual process of compiling code at run-time is much more complicated than what was described here in one sentence. It's also very much dependent on the exact language and runtime in question.)
Unlike scripted languages, compiled languages treat compilation and execution as two distinct steps. Compiling a program leaves you with an executable (often called a "binary"), a non-human readable file with machine code that the processor can run.
The nice thing about binaries is that they're generally distributable. So long as it was built with the right architecture in mind, you can copy an executable and run it immediately on other machines (like downloading a .exe file on Windows) without any need to share your source code or have the user perform any intermediate tasks before execution.
The problem with compiling is that it can be a massive pain, to put it lightly. I've argued that half the battle of learning a language like C++ is just getting your code to compile for the first time. There are many tools available to help you compile, ranging from barebones tools, such as g++ on Unix, to complex build systems that are integrated into IDEs like Visual Studio and Eclipse.
For this project, we decided to use a high-level build tool called CMake for the fact that it's fairly popular and cross-platform. CMake in and of itself, however, does not compile code. CMake results in compilation configurations. It depends on a lower-level build tool called Make to manage compiling from source. And then Make depends on a compiler to do the actual compiling.
Confused? Let's take a look at the subproducts and processes of compiling first. After that, the tooling choices for this project should make more sense.
Object Files Compiling source code, like a single .cpp file, results in something called an object file. An object file contains machine code but may not be executable in and of itself. Among other things, object files describe their own public APIs (usually called symbols) as well as references that need to be resolved from other object files. Depended upon object files might come from other source files within the same project or from external or system libraries.
In order to be executable, object files need to be linked together.
Linking Linking is the process of creating an executable by effectively combining object files. During the linking process, the linker (the thing that does the linking) resolves symbolic references between object files and outputs a self-contained binary with all the machine code needed to execute.
As an aside, linking is not required for all programs. Most operating systems allow dynamic linking, in which symbolic references point to libraries that are not compiled into the resulting binary. With dynamic linking, these references are resolved at runtime. An example of this is a program that depends on a system library. At runtime, the symbolic references of the program resolve to the symbols of the system library.
The Compilation Stack Let's go through the flow backwards from executable to source code. I'll show you the challenges that exist in each step in order to clarify why an abstraction is often helpful.
Compiling to Executable with a Compiler Technically, you only need a compiler to compile C++ source code to a binary. A compiler does the dirty work of writing machine code for a given processor architecture. There are many compilers available. For this project, we picked the open source GNU Compiler Collection, more commonly called G++ or GCC. gcc is a command line tool.
There are two challenges with using gcc alone, both of which relate to the fact that most C++ projects are large. For one thing, you need to pass the paths for all of the project's source header files and cpp files to gcc. This is in addition to any compiler flags or options. You can easily end up with single call to gcc that spans multiple lines on a terminal, which is unruly and error-prone.
Secondly, large projects will usually contain multiple linked binaries, each of which is compiled individually. If you're working in large project and only change one .cpp file, you generally only need to recompile that one binary - the rest of your project does not need to be compiled again! Compiling an entire project can take up to hours for large projects, and as such being intelligent about only compiling binaries that need to be compiled can save lots of time. GCC in and of itself is not smart enough to recognize what files in your project have changed and which haven't, and as such will recompile binaries needlessly - you'd need to manually change your gcc calls for the same optimizations. Luckily, there are tools that solve both of these problems!
- Clone this repo.
- Make a build directory:
mkdir build && cd build - Compile:
cmake .. && make- On windows, you may need to run:
cmake .. -G "Unix Makefiles" && make
- On windows, you may need to run:
- Run it:
./ExtendedKF
We've purposefully kept editor configuration files out of this repo in order to keep it as simple and environment agnostic as possible. However, we recommend using the following settings:
- indent using spaces
- set tab width to 2 spaces (keeps the matrices in source code aligned)
Please (do your best to) stick to Google's C++ style guide.
This is optional!
If you'd like to generate your own radar and lidar data, see the utilities repo for Matlab scripts that can generate additional data.
Note: regardless of the changes you make, your project must be buildable using cmake and make!
More information is only accessible by people who are already enrolled in Term 2 of CarND. If you are enrolled, see the project resources page for instructions and the project rubric.
-
You don't have to follow this directory structure, but if you do, your work will span all of the .cpp files here. Keep an eye out for TODOs.
-
Students have reported rapid expansion of log files when using the term 2 simulator. This appears to be associated with not being connected to uWebSockets. If this does occur, please make sure you are conneted to uWebSockets. The following workaround may also be effective at preventing large log files.
- create an empty log file
- remove write permissions so that the simulator can't write to log
-
Please note that the
Eigenlibrary does not initializeVectorXdorMatrixXdobjects with zeros upon creation.
Help your fellow students!
We decided to create Makefiles with cmake to keep this project as platform agnostic as possible. Similarly, we omitted IDE profiles in order to ensure that students don't feel pressured to use one IDE or another.
However! We'd love to help people get up and running with their IDEs of choice. If you've created a profile for an IDE that you think other students would appreciate, we'd love to have you add the requisite profile files and instructions to ide_profiles/. For example if you wanted to add a VS Code profile, you'd add:
- /ide_profiles/vscode/.vscode
- /ide_profiles/vscode/README.md
The README should explain what the profile does, how to take advantage of it, and how to install it.
Regardless of the IDE used, every submitted project must still be compilable with cmake and make.
A well written README file can enhance your project and portfolio. Develop your abilities to create professional README files by completing this free course.