Kalman Filter can be used to estimate the position, velocity of a moving object. The inputs to a Kalman Filter could be a noisy data that might be a representation of the object's position.
In this project lidar and radar measurements from a car to detect a moving object (eg. a bicycle) is used. By computing the Root Mean Square Error we could determine how good the algorithm is performing.
This project involves a Simulator which can be downloaded here
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
- 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
- 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
- It assumes a constant velocity and in reality a car might not be traveling at constant velocity