In this project I have utilized an Extended Kalman Filter algorithm 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 that the tolerance outlined in the project rubric.
- 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
The following graph compares real and estimated values for car coordinates using data from Dataset 1
The accuracy requirement is that the algortihm should perform with RMSE error lower than some threshold values. This shown in tables below for both datasets:
Dataset 1:
| Parameter | RMSE | RMSE threshold |
|---|---|---|
| x | 0.0974 | 0.11 |
| y | 0.0855 | 0.11 |
| Vx | 0.4517 | 0.52 |
| Vy | 0.4404 | 0.52 |
Dataset 2:
| Parameter | RMSE | RMSE threshold |
|---|---|---|
| x | 0.0726 | - |
| y | 0.0967 | - |
| Vx | 0.4579 | - |
| Vy | 0.4966 | - |