05. Program Gaussian Sampling: Code

/**
 * print_samples.cpp
 * 
 * Print out to the terminal 3 samples from a normal distribution with
 * mean equal to the GPS position and IMU heading measurements and
 * standard deviation of 2 m for the x and y position and 0.05 radians
 * for the heading of the car. 
 *
 * Author: Tiffany Huang
 */

#include <iostream>
#include <random> // Need this for sampling from distributions

using std::normal_distribution;

/**
 * Prints samples of x, y and theta from a normal distribution
 * @param gps_x   GPS provided x position
 * @param gps_y   GPS provided y position
 * @param theta   GPS provided yaw
 */
void printSamples(double gps_x, double gps_y, double theta);


int main() {
  
  // Set GPS provided state of the car.
  double gps_x = 4983;
  double gps_y = 5029;
  double theta = 1.201;
  
  // Sample from the GPS provided position.
  printSamples(gps_x, gps_y, theta);
  
  return 0;
}


void printSamples(double gps_x, double gps_y, double theta) {
  std::default_random_engine gen;
  double std_x, std_y, std_theta;  // Standard deviations for x, y, and theta

  // TODO: Set standard deviations for x, y, and theta


  // This line creates a normal (Gaussian) distribution for x
  normal_distribution<double> dist_x(gps_x, std_x);
  
  // TODO: Create normal distributions for y and theta


  for (int i = 0; i < 3; ++i) {
    double sample_x, sample_y, sample_theta;
    
    // TODO: Sample from these normal distributions like this: 
    //   sample_x = dist_x(gen);
    //   where "gen" is the random engine initialized earlier.
    
     
    // Print your samples to the terminal.
    std::cout << "Sample " << i + 1 << " " << sample_x << " " << sample_y << " " 
              << sample_theta << std::endl;
  }

  return;
}