The Monte Carlo Localization (MCL), or "particle filter localization", algorithm is used to estimate the position and orientation of a robot. Given a map of the environment, the algorithm estimates the position and orientation of a robot as it moves and senses the environment. The algorithm uses a particle filter to represent the distribution of likely states, with each particle representing a possible state (i.e. a hypothesis of where the robot is).
The algorithm starts with a uniform random distribution of particles over the configuration space, meaning the robot has no information about where it is and assumes it is equally likely to be at any point in space. Whenever the robot moves, it shifts the particles to predict its new state after the movement. Whenever the robot senses something, the particles are resampled based on recursive Bayesian estimation (i.e. how well the actual sensed data correlate with the predicted state). Ultimately, the particles should converge towards the actual position of the robot.
Clone the driver:
$ cd /home/workspace/
$ git clone https://github.com/barrymulvey/MCL_algorithm
$ cd MCL_algorithm/
Empty the Images folder:
$ rm -rf Images/*
Compile the program:
$ g++ main.cpp -o app -std=c++11 -I/usr/include/python2.7 -lpython2.7
Before running the program, ensure the Images folder is empty (step covered above).
$ ./appWait for the program to iterate 50 times.
After running the program, 50 images will be generated in the Images folder.
