05. Main.cpp

Here we will discuss the main.cpp file. Although you will not need to modify this file, the project is easier to implement once you understand what the file is doing. As a suggestion, open the github repository for the project and look at the code files simultaneously with this lecture slide.

Main.cpp

You do not need to modify the main.cpp , but let's discuss what the file does.

The Term 2 simulator is a client, and the c++ program software is a web server.

We already discussed how main.cpp reads in the sensor data. Recall that main.cpp reads in the sensor data line by line from the client and stores the data into a measurement object that it passes to the Kalman filter for processing. Also a ground truth list and an estimation list are used for tracking RMSE.

main.cpp is made up of several functions within main() , these all handle the uWebsocketIO communication between the simulator and it's self.

Here is the main protocol that main.cpp uses for uWebSocketIO in communicating with the simulator. 

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"]

All the main code loops in h.onMessage() , to have access to intial variables that we created at the beginning of main() , we pass pointers as arguments into the header of h.onMessage() .

For example: 

h.onMessage([&fusionEKF,&tools,&estimations,&ground_truth]
            (uWS::WebSocket<uWS::SERVER> ws, char *data, size_t length, 
             uWS::OpCode opCode)

The rest of the arguments in h.onMessage are used to set up the server. 

 // Create a Fusion EKF instance
  FusionEKF fusionEKF;

  // used to compute the RMSE later
  vector<VectorXd> estimations;
  vector<VectorXd> ground_truth;

  //Call the EKF-based fusion
  fusionEKF.ProcessMeasurement(meas_package);

The code is:

  • creating an instance of the FusionEKF class
  • Receiving the measurement data calling the ProcessMeasurement() function. ProcessMeasurement() is responsible for the initialization of the Kalman filter as well as calling the prediction and update steps of the Kalman filter. You will be implementing the ProcessMeasurement() function in FusionEKF.cpp :

Finally,

The rest of main.cpp will output the following results to the simulator:

  • estimation position
  • calculated RMSE

main.cpp will call a function to calculate root mean squared error: 

  // compute the accuracy (RMSE)
  Tools tools;
  cout << "Accuracy - RMSE:" << endl << tools.CalculateRMSE(estimations, ground_truth) << endl;

You will implement an RMSE function in the tools.cpp file.

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