def __init__(self,
bounds=(-10.0, 10.0, -10.0, 10.0),
resolution=0.2,
prior=0.5,
unknown=50.0,
decay=0.00001,
num_angles=1):
""" bounds: the extends of the map, in m
resolution: the size of each grid cell, in m """
assert (bounds[1] > bounds[0])
assert (bounds[3] > bounds[2])
assert (resolution > 0)
numx = int(math.ceil((bounds[1] - bounds[0]) / resolution))
numy = int(math.ceil((bounds[3] - bounds[2]) / resolution))
tot_prod = int(numx * numy * num_angles)
self.shape = (numy, numx, num_angles)
self.bounds = bounds
self.resolution = resolution
self.unknown = unknown
big_covar = unknown #1000.0
small_covar = decay #10^-5
p_occ = logodds(prior)
# C, R matrix will be changed for every measurement, these
# are just place holder values
x0 = p_occ * numpy.ones((tot_prod, 1))
P0 = big_covar * numpy.ones((tot_prod, 1))
Q = small_covar * numpy.ones((tot_prod, 1))
R = numpy.ones((tot_prod, 1))
self.kf = KalmanSmootherFast(x0=x0, P0=P0, Q=Q, R=R)
def __init__(self, bounds=(-10.0, 10.0, -10.0, 10.0), resolution=0.2,
prior=0.5, unknown=50.0, decay=0.00001, num_angles=1):
""" bounds: the extends of the map, in m
resolution: the size of each grid cell, in m """
assert(bounds[1] > bounds[0])
assert(bounds[3] > bounds[2])
assert(resolution > 0)
numx = int(math.ceil((bounds[1] - bounds[0])/resolution))
numy = int(math.ceil((bounds[3] - bounds[2])/resolution))
tot_prod = int(numx*numy*num_angles)
self.shape = (numy, numx, num_angles)
self.bounds = bounds
self.resolution = resolution
self.unknown = unknown
big_covar = unknown #1000.0
small_covar = decay #10^-5
p_occ = logodds(prior)
# C, R matrix will be changed for every measurement, these
# are just place holder values
x0 = p_occ*numpy.ones((tot_prod,1))
P0 = big_covar*numpy.ones((tot_prod,1))
Q = small_covar*numpy.ones((tot_prod,1))
R = numpy.ones((tot_prod,1))
self.kf = KalmanSmootherFast(x0=x0, P0=P0, Q=Q, R=R)
class OccupancyGrid(object):
""" Occupancy grid type map """
def __init__(self,
bounds=(-10.0, 10.0, -10.0, 10.0),
resolution=0.2,
prior=0.5,
unknown=50.0,
decay=0.00001,
num_angles=1):
""" bounds: the extends of the map, in m
resolution: the size of each grid cell, in m """
assert (bounds[1] > bounds[0])
assert (bounds[3] > bounds[2])
assert (resolution > 0)
numx = int(math.ceil((bounds[1] - bounds[0]) / resolution))
numy = int(math.ceil((bounds[3] - bounds[2]) / resolution))
tot_prod = int(numx * numy * num_angles)
self.shape = (numy, numx, num_angles)
self.bounds = bounds
self.resolution = resolution
self.unknown = unknown
big_covar = unknown #1000.0
small_covar = decay #10^-5
p_occ = logodds(prior)
# C, R matrix will be changed for every measurement, these
# are just place holder values
x0 = p_occ * numpy.ones((tot_prod, 1))
P0 = big_covar * numpy.ones((tot_prod, 1))
Q = small_covar * numpy.ones((tot_prod, 1))
R = numpy.ones((tot_prod, 1))
self.kf = KalmanSmootherFast(x0=x0, P0=P0, Q=Q, R=R)
def __call__(self, sonar_measurement):
indices = sonar_measurement.indices
# Convert probabilities to log odds format first
prob = logodds(sonar_measurement.prob)
prob = prob[:, numpy.newaxis]
n = sonar_measurement.var.shape[0]
R = numpy.reshape(numpy.asarray(sonar_measurement.var), (n, 1))
ind = indices[:, 2] * self.shape[1] * self.shape[
0] + indices[:, 1] * self.shape[0] + indices[:, 0]
return self.kf.meas_update(prob, ind, R)
def eval_measurement(self, cells, dist):
""" Evaluate measurements against existing map """
def ownpdf(x, m, sigma):
""" Non-normalized pdf for cell/sensor measurement
x - eval points
m - mean, either scaler or same shape as x
sigma - 'width' of distribution """
pdf = numpy.zeros(numpy.shape(x))
top_ind = (abs(x - m) <= sigma / 2.0)
if (top_ind.any()):
pdf[top_ind] = 1.0
interp_ind = (abs(x - m) > sigma / 2.0) & (abs(x - m) < sigma)
if (interp_ind.any()):
pdf[interp_ind] = (1.0 - (abs(x - m)[interp_ind] - sigma / 2) /
(sigma / 2))
return pdf
num_angles = self.shape[2]
cells.sort(order='r', axis=0)
tmp = (cells['a_w'] + math.pi) * num_angles / (2.0 * math.pi)
tmp = numpy.floor(tmp)
# Wrap-around
tmp[tmp >= num_angles] = num_angles - 1
tmp = numpy.reshape(tmp, (-1, 1))
indices = numpy.concatenate((cells['opt'], tmp), 1)
ind = indices[:, 2] * self.shape[1] * self.shape[
0] + indices[:, 1] * self.shape[0] + indices[:, 0]
ind = ind.astype(int)
pdf = ownpdf(cells['r'], dist, self.resolution)
prob_occ = inv_logodds(self.kf.x_new[ind]).ravel()
#multiplier = 1.0
#prob = 0
#for j in range(numpy.shape(cells)[0]):
# prob += multiplier*prob_occ[j]*pdf[j]
# multiplier *= (1 - prob_occ[j])
# Same calculations as for loop above, but much more efficient
multiplier = numpy.cumprod(1 - prob_occ)
multiplier = numpy.roll(multiplier, 1)
multiplier[0] = 1.0
prob = numpy.dot(multiplier, prob_occ * pdf)
return prob
def time_update(self):
self.kf.time_update()
def smooth(self, z_next):
self.kf.smooth(z_next.kf.x_new, z_next.kf.P)
def get_nav_map(self):
tmp_prob = numpy.reshape(numpy.asarray(self.kf.x_new),
(-1, self.shape[2]),
order='F')
tmp_var = numpy.reshape(numpy.asarray(self.kf.P), (-1, self.shape[2]),
order='F')
tmp_prob = numpy.copy(tmp_prob)
tmp_prob[tmp_var >= self.unknown] = -numpy.Inf
ind = numpy.argmax(tmp_prob, axis=1)
tmp_prob = tmp_prob[numpy.arange(tmp_prob.shape[0]), ind]
tmp_var = tmp_var[numpy.arange(tmp_var.shape[0]), ind]
tmp_prob = tmp_prob.reshape((self.shape[0], self.shape[1]))
tmp_var = tmp_var.reshape((self.shape[0], self.shape[1]))
return (inv_logodds(tmp_prob), tmp_var)
def find_visible_cells(self, cone):
""" Finds all cells centers of all grids within the cone specified
state - (x,y,dir)
prec - FOV width, summetric around dir
dist - max distance to search """
def course_crop(dim, bounds, cone):
""" Rectangular bounds on which indices need to be evaluated """
(xmin, xmax, ymin, ymax) = cone.get_bounding_rect()
xleft = math.floor(
(dim[1] - 1) * (xmin - bounds[0]) / (bounds[1] - bounds[0]))
xright = math.ceil(
(dim[1] - 1) * (xmax - bounds[0]) / (bounds[1] - bounds[0]))
xleft = max((xleft, 1))
xright = min((xright, dim[1] - 1))
yupp = math.floor(
(dim[0] - 1) * (ymin - bounds[2]) / (bounds[3] - bounds[2]))
ylow = math.ceil(
(dim[0] - 1) * (ymax - bounds[2]) / (bounds[3] - bounds[2]))
yupp = max((yupp, 1))
ylow = min((ylow, dim[0] - 1))
return (int(xleft), int(xright), int(yupp), int(ylow))
(xle, xri, yup, ylo) = course_crop(self.shape, self.bounds, cone)
# Evaluate each cell center for the FOV
x_ind = numpy.arange(xle, xri + 1, dtype=numpy.int)
y_ind = numpy.arange(yup, ylo + 1, dtype=numpy.int)
x, y = numpy.meshgrid(x_ind * self.resolution + self.bounds[0],
y_ind * self.resolution + self.bounds[2])
x_ind, y_ind = numpy.meshgrid(x_ind, y_ind)
x = x.reshape((-1, 1))
y = y.reshape((-1, 1))
x_ind = x_ind.reshape((-1, 1))
y_ind = y_ind.reshape((-1, 1))
if (x.shape[0] == 0):
return None
# cells will contains (a,r,y_ind,x_ind) for all visible cells
cells = cone(y[:, 0], x[:, 0], numpy.hstack([y_ind, x_ind]))
if (cells == None):
return None
cells.sort(order='r', axis=0)
return cells
class OccupancyGrid(object):
""" Occupancy grid type map """
def __init__(self, bounds=(-10.0, 10.0, -10.0, 10.0), resolution=0.2,
prior=0.5, unknown=50.0, decay=0.00001, num_angles=1):
""" bounds: the extends of the map, in m
resolution: the size of each grid cell, in m """
assert(bounds[1] > bounds[0])
assert(bounds[3] > bounds[2])
assert(resolution > 0)
numx = int(math.ceil((bounds[1] - bounds[0])/resolution))
numy = int(math.ceil((bounds[3] - bounds[2])/resolution))
tot_prod = int(numx*numy*num_angles)
self.shape = (numy, numx, num_angles)
self.bounds = bounds
self.resolution = resolution
self.unknown = unknown
big_covar = unknown #1000.0
small_covar = decay #10^-5
p_occ = logodds(prior)
# C, R matrix will be changed for every measurement, these
# are just place holder values
x0 = p_occ*numpy.ones((tot_prod,1))
P0 = big_covar*numpy.ones((tot_prod,1))
Q = small_covar*numpy.ones((tot_prod,1))
R = numpy.ones((tot_prod,1))
self.kf = KalmanSmootherFast(x0=x0, P0=P0, Q=Q, R=R)
def __call__(self, sonar_measurement):
indices = sonar_measurement.indices
# Convert probabilities to log odds format first
prob = logodds(sonar_measurement.prob)
prob = prob[:, numpy.newaxis]
n = sonar_measurement.var.shape[0]
R = numpy.reshape(numpy.asarray(sonar_measurement.var),(n,1))
ind = indices[:, 2]*self.shape[1]*self.shape[0] + indices[:, 1]*self.shape[0]+indices[:, 0]
return self.kf.meas_update(prob, ind, R)
def eval_measurement(self, cells, dist):
""" Evaluate measurements against existing map """
def ownpdf(x, m, sigma):
""" Non-normalized pdf for cell/sensor measurement
x - eval points
m - mean, either scaler or same shape as x
sigma - 'width' of distribution """
pdf = numpy.zeros(numpy.shape(x))
top_ind = (abs(x-m) <= sigma/2.0)
if (top_ind.any()):
pdf[top_ind] = 1.0
interp_ind = (abs(x-m) > sigma/2.0) & (abs(x-m) < sigma)
if (interp_ind.any()):
pdf[interp_ind] = (
1.0 - (abs(x-m)[interp_ind]-sigma/2)/(sigma/2)
)
return pdf
num_angles = self.shape[2]
cells.sort(order='r', axis=0)
tmp = (cells['a_w'] + math.pi)*num_angles/(2.0*math.pi)
tmp = numpy.floor(tmp)
# Wrap-around
tmp[tmp >= num_angles] = num_angles-1
tmp = numpy.reshape(tmp,(-1,1))
indices = numpy.concatenate((cells['opt'], tmp), 1)
ind = indices[:, 2]*self.shape[1]*self.shape[0] + indices[:, 1]*self.shape[0]+indices[:, 0]
ind = ind.astype(int)
pdf = ownpdf(cells['r'], dist, self.resolution)
prob_occ = inv_logodds(self.kf.x_new[ind]).ravel()
#multiplier = 1.0
#prob = 0
#for j in range(numpy.shape(cells)[0]):
# prob += multiplier*prob_occ[j]*pdf[j]
# multiplier *= (1 - prob_occ[j])
# Same calculations as for loop above, but much more efficient
multiplier = numpy.cumprod(1 - prob_occ)
multiplier = numpy.roll(multiplier, 1)
multiplier[0] = 1.0
prob = numpy.dot(multiplier, prob_occ*pdf)
return prob
def time_update(self):
self.kf.time_update()
def smooth(self, z_next):
self.kf.smooth(z_next.kf.x_new, z_next.kf.P)
def get_nav_map(self):
tmp_prob = numpy.reshape(numpy.asarray(self.kf.x_new),
(-1,self.shape[2]), order='F')
tmp_var = numpy.reshape(numpy.asarray(self.kf.P),
(-1,self.shape[2]), order='F')
tmp_prob = numpy.copy(tmp_prob)
tmp_prob[tmp_var >= self.unknown] = -numpy.Inf
ind = numpy.argmax(tmp_prob,axis=1)
tmp_prob = tmp_prob[numpy.arange(tmp_prob.shape[0]), ind]
tmp_var = tmp_var[numpy.arange(tmp_var.shape[0]), ind]
tmp_prob = tmp_prob.reshape((self.shape[0],self.shape[1]))
tmp_var = tmp_var.reshape((self.shape[0],self.shape[1]))
return (inv_logodds(tmp_prob), tmp_var)
def find_visible_cells(self, cone):
""" Finds all cells centers of all grids within the cone specified
state - (x,y,dir)
prec - FOV width, summetric around dir
dist - max distance to search """
def course_crop(dim, bounds, cone):
""" Rectangular bounds on which indices need to be evaluated """
(xmin, xmax, ymin, ymax) = cone.get_bounding_rect()
xleft = math.floor((dim[1]-1)*
(xmin-bounds[0])/(bounds[1]-bounds[0]))
xright = math.ceil((dim[1]-1)*
(xmax-bounds[0])/(bounds[1]-bounds[0]))
xleft = max((xleft, 1))
xright = min((xright, dim[1]-1))
yupp = math.floor((dim[0]-1)*
(ymin-bounds[2])/(bounds[3]-bounds[2]))
ylow = math.ceil((dim[0]-1)*
(ymax-bounds[2])/(bounds[3]-bounds[2]))
yupp = max((yupp, 1))
ylow = min((ylow, dim[0]-1))
return (int(xleft), int(xright), int(yupp), int(ylow))
(xle, xri, yup, ylo) = course_crop(self.shape, self.bounds, cone)
# Evaluate each cell center for the FOV
x_ind = numpy.arange(xle, xri+1, dtype=numpy.int)
y_ind = numpy.arange(yup, ylo+1, dtype=numpy.int)
x, y = numpy.meshgrid(x_ind*self.resolution+self.bounds[0],
y_ind*self.resolution+self.bounds[2])
x_ind, y_ind = numpy.meshgrid(x_ind, y_ind)
x = x.reshape((-1, 1))
y = y.reshape((-1, 1))
x_ind = x_ind.reshape((-1, 1))
y_ind = y_ind.reshape((-1, 1))
if (x.shape[0] == 0):
return None
# cells will contains (a,r,y_ind,x_ind) for all visible cells
cells = cone(y[:, 0], x[:, 0], numpy.hstack([y_ind, x_ind]))
if (cells == None):
return None
cells.sort(order='r', axis=0)
return cells