Particle Filter

General Info

The robot has been abducted and taken to a new place! Fortunately, it contains a map of this area, a (noisy) GPS estimate of its original location, and a variety of (noisy) sensor and control data.

In this project, I need to write a C++ program to build a two-dimensional particle filter. The particle filter will be given a map and some preliminary localization data (analogous to what a GPS would provide). The filter will receive observation and control data at each time step.

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

• sense noisy position data from the simulator
  • [“sense_x”]
  • [“sense_y”]
  • [“sense_theta”]
• get the previous velocity and yaw rate to predict the particle’s transitioned state
  • [“previous_velocity”]
  • [“previous_yawrate”]
• receive noisy observation data from the simulator, in a respective list of x/y values
  • [“sense_observations_x”]
  • [“sense_observations_y”]

OUTPUT: values provided by the c++ program to the simulator

• best particle values used for calculating the error evaluation
  • [“best_particle_x”]
  • [“best_particle_y”]
  • [“best_particle_theta”]

• Optional message data used for debugging particle’s sensing and associations

  • for respective (x,y) sensed positions ID label
    • [“best_particle_associations”]
  • for respective (x,y) sensed positions
    • [“best_particle_sense_x”] <= list of sensed x positions
    • [“best_particle_sense_y”] <= list of sensed y positions

Your job is to build out the methods in particle_filter.cpp until the simulator output says:

Success! Your particle filter passed!

Output

This algorithm performance depends on how many particles we used. The error tends to be less if we have more particles. However, it comes with a computational cost. Below are the result of my experiments.