Commented out unimplemented laser model functions

src/cs4313_utilities/robot_math.py

@@ -177,72 +177,73 @@ def likelihoodFieldModel(distance, z_max, sigma, alpha_hit, alpha_rand):
     return alpha_hit * gaussian_PDF(distance, sigma) + alpha_rand / z_max
 
 
-def rangeSensorModel(z, z_exp, z_max, sigma, lamda, \
-                     alpha_hit, alpha_unexp, alpha_max, alpha_rand):
-    ''' Implements the range sensor model from the CS4313 lecture material
-    @param z: sensor reading (range) being checked
-    @param z_exp: expected value (i.e., expected range to the obstacle)
-    @param z_max: sensor's maximum range
-    @param sigma: standard deviation of the hit model Gaussian
-    @param lamda: rate parameter of the obstacle model exponential
-    @param alpha_hit: normalized coefficient for the hit model
-    @param alpha_unexp: normalized coefficient for the obstacle model
-    @param alpha_max: normalized coefficient for the maximum range model
-    @param alpha_rand: normalized coefficient for the random return model
-    @return: PDF value for the probability that the return corresponds to the map
-    '''
-    return alpha_hit * rangeSensorHitModel(z, z_exp, sigma) + \
-           alpha_unexp * rangeSensorObstacleModel(z, z_exp, lamda) + \
-           alpha_rand * rangeSensorRandomModel(z_max) + \
-           alpha_max * rangeSensorMaxRangeModel(z, z_max)
-
-
-def rangeSensorHitModel(z, z_exp, sigma):
-    ''' Computes the probability that a given sensor reading is from a mapped
-    obstacle using a Gaussian distribution centered on the expected range
-    @param z: sensor reading (range) being checked
-    @param z_exp: expected value (i.e., expected range to the obstacle)
-    @param sigma: standard deviation of the Gaussian
-    @return: the PDF value that the return is from a mapped obstacle
-    '''
-    return gaussian_PDF((z-z_exp), sigma)
-
-
-def rangeSensorObstacleModel(z, z_exp, lamda):
-    ''' Computes the probability of a given sensor reading is from an
-    unmapped obstacle using an exponential distribution
-    @param z: sensor reading (range) being checked
-    @param z_exp: expected value (i.e., expected range to the obstacle)
-    @param lamda: rate parameter of the distribution
-    @return: the PDF value that the return is from an unmapped obstacle
-    '''
-    if z > z_exp:
-        return 0.0  # Can't detect an obstacle beyond the mapped one
-
-    exponential = math.exp(-lamda * z)
-    eta = 1.0 / (1.0 - exponential)
-    return eta * lamda * exponential
-
-
-def rangeSensorRandomModel(z_max):
-    ''' Computes the probability that a return is just a random return
-    using a uniform distribution between 0 and the maximum range
-    @param z_max: sensor's maximum range
-    @return: PDF value that the return is random
-    '''
-    return 1.0 / z_max
-
-
-def rangeSensorMaxRangeModel(z, z_max):
-    ''' Computes the probability that a return is a maximum range return
-    (i.e., not object detected)
-    @param z: sensor reading (range) being checked
-    @param z_max: maximum range of the sensor
-    @return the PDF value that the specific return was a max-range return
-    '''
-    if abs(z - z_max) <= SMALLNUM:
-        return 1.0
-    return 0.0
+# NOTE: Model not implemented yet (commented out for now)
+#def rangeSensorModel(z, z_exp, z_max, sigma, lamda, \
+#                     alpha_hit, alpha_unexp, alpha_max, alpha_rand):
+#    ''' Implements the range sensor model from the CS4313 lecture material
+#    @param z: sensor reading (range) being checked
+#    @param z_exp: expected value (i.e., expected range to the obstacle)
+#    @param z_max: sensor's maximum range
+#    @param sigma: standard deviation of the hit model Gaussian
+#    @param lamda: rate parameter of the obstacle model exponential
+#    @param alpha_hit: normalized coefficient for the hit model
+#    @param alpha_unexp: normalized coefficient for the obstacle model
+#    @param alpha_max: normalized coefficient for the maximum range model
+#    @param alpha_rand: normalized coefficient for the random return model
+#    @return: PDF value for the probability that the return corresponds to the map
+#    '''
+#    return alpha_hit * rangeSensorHitModel(z, z_exp, sigma) + \
+#           alpha_unexp * rangeSensorObstacleModel(z, z_exp, lamda) + \
+#           alpha_rand * rangeSensorRandomModel(z_max) + \
+#           alpha_max * rangeSensorMaxRangeModel(z, z_max)
+#
+#
+#def rangeSensorHitModel(z, z_exp, sigma):
+#    ''' Computes the probability that a given sensor reading is from a mapped
+#    obstacle using a Gaussian distribution centered on the expected range
+#    @param z: sensor reading (range) being checked
+#    @param z_exp: expected value (i.e., expected range to the obstacle)
+#    @param sigma: standard deviation of the Gaussian
+#    @return: the PDF value that the return is from a mapped obstacle
+#    '''
+#    return gaussian_PDF((z-z_exp), sigma)
+#
+#
+#def rangeSensorObstacleModel(z, z_exp, lamda):
+#    ''' Computes the probability of a given sensor reading is from an
+#    unmapped obstacle using an exponential distribution
+#    @param z: sensor reading (range) being checked
+#    @param z_exp: expected value (i.e., expected range to the obstacle)
+#    @param lamda: rate parameter of the distribution
+#    @return: the PDF value that the return is from an unmapped obstacle
+#    '''
+#    if z > z_exp:
+#        return 0.0  # Can't detect an obstacle beyond the mapped one
+#
+#    exponential = math.exp(-lamda * z)
+#    eta = 1.0 / (1.0 - exponential)
+#    return eta * lamda * exponential
+#
+#
+#def rangeSensorRandomModel(z_max):
+#    ''' Computes the probability that a return is just a random return
+#    using a uniform distribution between 0 and the maximum range
+#    @param z_max: sensor's maximum range
+#    @return: PDF value that the return is random
+#    '''
+#    return 1.0 / z_max
+#
+#
+#def rangeSensorMaxRangeModel(z, z_max):
+#    ''' Computes the probability that a return is a maximum range return
+#    (i.e., not object detected)
+#    @param z: sensor reading (range) being checked
+#    @param z_max: maximum range of the sensor
+#    @return the PDF value that the specific return was a max-range return
+#    '''
+#    if abs(z - z_max) <= SMALLNUM:
+#        return 1.0
+#    return 0.0
 
 
 def projectSensorReturnToWorld(state, sensor_posit, rng, brg):