Estimate the location of a moving object by using a particle filter.
The program is written in C++. This Project is from Udacity's Self-Driving Car Engineer Nanodegree Program.
- Clone this repo.
- Make sure uWebSocketIO is installed. Two install scripts are included for MAC and Linux. For Windows use Windows 10 Bash on Ubuntu and following the Linux instructions.
- Make a build directory:
mkdir build && cd build - Compile:
cmake .. && make - Run the programs: Run
./particle_filter. Open Term 2 Simulator and run the corresponding page.
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"]
["best_particle_sense_y"]
You can find the inputs to the particle filter in the data directory. The file map_data.txt includes the position of landmarks (in meters) on an arbitrary Cartesian coordinate system. Each row has three columns