ekf
Extended Kalman Filter for Pedestrian Tracking
The source files in this repository are derived from this Udacity repo.
The objective of this project is to develop a Kalman filter that is able to estimate the position and velocity of an object for which we have sensor measurements available. The measurements, however, originate from two different sources, "LIDAR" and "RADAR", which leads to the problem that for the first sensor, we only have position measurements in kartesian coordinates, while for the second sensor, we have position measurements in radial coordinates as well as a radial velocity. This leads to a sensor fusion problem, the solution of which also is included in this project.
Dependencies
- 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]((https://developer.apple.com/xcode/features/)
- Windows: recommend using MinGW
Basic Build Instructions
- 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 path/to/input.txt path/to/output.txt. You can find some sample inputs in 'data/'.- eg.
./ExtendedKF ../data/sample-laser-radar-measurement-data-1.txt output.txt
- eg.