Classes | Functions | Variables
pfilter::pfilter Namespace Reference

Classes

class  PFilter

Functions

def likelihood
def normalize_likelihood
def predict
def resample_uss
def retFalse
def retTrue
 Functions implementing a particle filter.
def set_norm_likelihood
def set_resample_uss

Variables

dictionary counts = {}
tuple new_particles = resample_uss(num_particles, normalized)
tuple normalized = normalize_likelihood(particles)
int num_particles = 1000
list particles = [("4", 4), ("1",1), ("2",2), ("3", 3)]

Function Documentation

def pfilter.pfilter.likelihood (   appearance_model,
  measurement,
  particle_set 
)
Evaluate using appearance model 

Definition at line 75 of file pfilter.py.

def pfilter.pfilter.normalize_likelihood (   weighted_particles)
Make all the particle weights sum up to 1 

Definition at line 138 of file pfilter.py.

def pfilter.pfilter.predict (   motion_model,
  control_input,
  particle_set 
)
Predict using motion model 

Definition at line 63 of file pfilter.py.

def pfilter.pfilter.resample_uss (   num_samples,
  particles 
)
    Universal stochastic sampler (low variance resampling)
    num_samples - number of samples desired
    particles   - pairs of (state, weight) tuples

Definition at line 86 of file pfilter.py.

def pfilter.pfilter.retFalse (   args)

Definition at line 29 of file pfilter.py.

def pfilter.pfilter.retTrue (   args)

Functions implementing a particle filter.

Note: To instantiate a particle filter you will need a motion and appearance model. Below are signatures and description of the motion and appearance models: (optional) motion.make_set: (int) -> list state motion.predict: (control, state) -> state appearance.weight: (measurement, state) -> double

Where what is considered a 'state' must agree between the motion and appearance classes.

Optional: The particle filter can be sped up by defining additional functions: * weight_partial - partial application * weight_set - any other optimizations

* predict_partial - partial application

Definition at line 26 of file pfilter.py.

def pfilter.pfilter.set_norm_likelihood (   weighted_particles)

Definition at line 150 of file pfilter.py.

def pfilter.pfilter.set_resample_uss (   num_samples,
  particles 
)
    Universal stochastic sampler (low variance resampling)
    num_samples - number of samples desired
    particles   - pairs of (state, weight) tuples

Definition at line 108 of file pfilter.py.


Variable Documentation

dictionary pfilter::pfilter::counts = {}

Definition at line 168 of file pfilter.py.

Definition at line 165 of file pfilter.py.

Definition at line 161 of file pfilter.py.

Definition at line 164 of file pfilter.py.

list pfilter::pfilter::particles = [("4", 4), ("1",1), ("2",2), ("3", 3)]

Definition at line 160 of file pfilter.py.



pfilter
Author(s): Travis Deyle, Hai Nguyen, Advisor: Prof. Charlie Kemp, Lab: Healthcare Robotics Lab at Georgia Tech
autogenerated on Wed Nov 27 2013 11:42:09