CarND-Term2-P1-Extended-Kalman-Filter

Overview

In this project, you are going to implement the extended Kalman filter in C++. You will be tracking the bicycle's position and velocity and predicting the positon of the object.
There is a simulator provided by Udacity (Term 2 Simulator Release) which can generate noisy LIDAR and RADAR measurements. And you will be using those measurements to predict your object position.
Here is the link to the orginal repository provided by Udaciy.

Prerequisites/Dependencies

cmake: 3.5
- All OSes: click here for installation instructions
make: 4.1 (Linux and 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

Setup Instructions (abbreviated)

Meet the Prerequisites/Dependencies
Intall uWebSocketIO on your system
2.1 Windows Installation
2.1.1 Use latest version of Ubuntu Bash 16.04 on Windows 10, here is the step-by-step guide for setting up the utility.
2.1.2 (Optional) Check your version of Ubuntu Bash here.
Open Ubuntu Bash and clone the project repository
On the command line execute ./install-ubuntu.sh
Build and run your code.

Project Description

starter code in the src folder
a README file with instructions on compiling the code
a Docs folder, which contains details about the structure of the code templates
CMakeLists.txt file that will be used when compiling your code (you do not need to change this file)
a data file for testing your extended Kalman filter which the simulator interface provides

Run the project

Clone this respository
At the top level of the project repository, create a build directory: mkdir build && cd build
In /build directory, compile yoru code with cmake .. && make
Launch the simulator from Windows
Execute the run command for the project ./ExtendedKF (Make sure you also run the simulator on the Windows host machine) If you see * * this message, it is working Listening to port 4567 Connected!!!

Project Rubric

1. Compiling

1.1 Your code should compile.

Compiled successfully.

2. Accuracy

2.2 px, py, vx, vy output coordinates must have an RMSE <= [.11, .11, 0.52, 0.52] when using the file: "obj_pose-laser-radar-synthetic-input.txt" which is the same data file the simulator uses for Dataset 1.

Meet

3. Follows the Correct Algorithm

3.1 Your Sensor Fusion algorithm follows the general processing flow as taught in the preceding lessons.

My Kalman Filter implementation is completed at kalman_filter.cpp

3.2 Your Kalman Filter algorithm handles the first measurements appropriately.

The first measurement is handled at FusionEKF.cpp Line79-130

3.3 Your Kalman Filter algorithm first predicts then updates.

My Kalman Filter predict function will be called at FusionEKF.cpp Line132-166
My Kalman Filter update function will be called after predict function at FusionEKF.cpp Line135-192

3.4 Your Kalman Filter can handle radar and lidar measurements.

My Kalman Filter update function will handle them at FusionEKF.cpp Line135-192

4. Code Efficiency

4.1 Your algorithm should avoid unnecessary calculations.

Yes.

Code Style

Please (do your best to) stick to Google's C++ style guide.

Videos

Video recordings for success cases.
Success to plan path on dataset 1.

Success to plan path on dataset 2.

jinchaolu / carnd-term2-p1-extended-kalman-filter Goto Github PK