Self-Driving Car Engineer Nanodegree Program
In this project, I utilize a kalman filter to estimate the state of a moving object of interest with noisy lidar and radar measurements. In the project rubric, I need to acheive the obtained RMSE values that are lower than the tolerance .
This project involves the Term 2 Simulator which can be downloaded here
- main.cpp
- FusionEKF.cpp
- kalman_filter.cpp
- tool.cpp
What the functions of the files:
| Items | Description |
|---|---|
| main.cpp | Communicateing with the Term 2 Simulator receiving data measurements, calling a function to run the Kalman filter, calling a function to calculate RMSE. It reads in the data and sends a sensor measurement to FusionEKF.cpp |
| FusionEKF.cpp | Initializeing the filter, calls the predict function, calls the update function. It takes the sensor data and initializes variables and updates variables. |
| kalman_filter.cpp | Defines the predict function, the update function for lidar, and the update function for radar. It has a variable called ekf_, which is an instance of a KalmanFilter class. |
| tools.cpp | function to calculate RMSE. |
The ekf_ will hold the matrix and vector values. You will also use the ekf_ instance to call the predict and update equations. The KalmanFilter class is defined in kalman_filter.cpp and kalman_filter.h.
- mkdir build
- cd build
- cmake ..
- make
- ./ExtendedKF
DEMO video: https://youtu.be/Ge-iiLgHQFs
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"]
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