Your robot has been kidnapped and transported to a new location! Luckily it has a map of this location, a (noisy) GPS estimate of its initial location, and lots of (noisy) sensor and control data.
In this project you will implement a 2 dimensional particle filter in C++. Your particle filter will be given a map and some initial localization information (analogous to what a GPS would provide). At each time step your filter will also get observation and control data.
This project involves the Term 2 Simulator which can be downloaded here
Note that the programs that need to be written to accomplish the project are src/particle_filter.cpp, and particle_filter.h
The program main.cpp has already been filled out, but feel free to modify it.
Here is the main protcol 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
map_data.txt includes the position of landmarks (in meters) on an arbitrary Cartesian coordinate system. Each row has three columns
- x position
- y position
- landmark id