def run_spline_epipolar():
# Construct symbolic problem
num_landmarks = 10
num_frames = 3
num_imu_readings = 8
bezier_degree = 4
out = 'out/epipolar_accel_bezier3'
if not os.path.isdir(out):
os.mkdir(out)
# Both splines should start at 0,0,0
frame_times = np.linspace(0, .9, num_frames)
imu_times = np.linspace(0, 1, num_imu_readings)
true_rot_controls = np.random.rand(bezier_degree-1, 3)
true_pos_controls = np.random.rand(bezier_degree-1, 3)
true_landmarks = np.random.randn(num_landmarks, 3)
true_cayleys = np.array([zero_offset_bezier(true_rot_controls, t) for t in frame_times])
true_positions = np.array([zero_offset_bezier(true_pos_controls, t) for t in frame_times])
true_accels = np.array([zero_offset_bezier_second_deriv(true_pos_controls, t) for t in imu_times])
true_qs = map(cayley_mat, true_cayleys)
true_rotations = map(cayley, true_cayleys)
true_uprojections = [[np.dot(R, x-p) for x in true_landmarks]
for R,p in zip(true_rotations, true_positions)]
true_projections = [[normalized(zu) for zu in row] for row in true_uprojections]
p0 = true_positions[0]
q0 = true_qs[0]
for i in range(1, num_frames):
p = true_positions[i]
q = true_qs[i]
E = essential_matrix(q0, p0, q, p)
for j in range(num_landmarks):
z = true_projections[i][j]
z0 = true_projections[0][j]
#print np.dot(z, np.dot(E, z0))
# construct symbolic versions of the above
s_offs = 0
p_offs = s_offs + (bezier_degree-1)*3
num_vars = p_offs + (bezier_degree-1)*3
sym_vars = [Polynomial.coordinate(i, num_vars, Fraction) for i in range(num_vars)]
sym_rot_controls = np.reshape(sym_vars[s_offs:s_offs+(bezier_degree-1)*3], (bezier_degree-1, 3))
sym_pos_controls = np.reshape(sym_vars[p_offs:p_offs+(bezier_degree-1)*3], (bezier_degree-1, 3))
true_vars = np.hstack((true_rot_controls.flatten(),
true_pos_controls.flatten()))
residuals = []
# Accel residuals
for i in range(num_imu_readings):
sym_a = zero_offset_bezier_second_deriv(sym_pos_controls, imu_times[i])
residual = sym_a - true_accels[i]
residuals.extend(residual)
# Epipolar residuals
p0 = np.zeros(3)
R0 = np.eye(3)
for i in range(1, num_frames):
sym_s = zero_offset_bezier(sym_rot_controls, frame_times[i])
sym_p = zero_offset_bezier(sym_pos_controls, frame_times[i])
sym_q = cayley_mat(sym_s)
#sym_q = np.eye(3) * (1. - np.dot(sym_s, sym_s)) + 2.*skew(sym_s) + 2.*np.outer(sym_s, sym_s)
sym_E = essential_matrix(R0, p0, sym_q, sym_p)
for j in range(num_landmarks):
z = true_projections[i][j]
z0 = true_projections[0][j]
residual = np.dot(z, np.dot(sym_E, z0))
residuals.append(residual)
print 'Num vars:',num_vars
print 'Num residuals:',len(residuals)
print 'Residuals:', len(residuals)
cost = Polynomial(num_vars)
for r in residuals:
cost += r*r
print ' %f (degree=%d, length=%d)' % (r(*true_vars), r.total_degree, len(r))
print '\nCost:'
print ' Num terms: %d' % len(cost)
print ' Degree: %d' % cost.total_degree
print '\nGradients:'
gradients = cost.partial_derivatives()
for gradient in gradients:
print ' %d (degree=%d, length=%d)' % (gradient(*true_vars), gradient.total_degree, len(gradient))
jacobians = [r.partial_derivatives() for r in residuals]
J = np.array([[J_ij(*true_vars) for J_ij in row] for row in jacobians])
U, S, V = np.linalg.svd(J)
print '\nJacobian singular values:'
print J.shape
print S
null_space_dims = sum(np.abs(S) < 1e-5)
if null_space_dims > 0:
print '\nNull space:'
for i in null_space_dims:
print V[-i]
print V[-2]
print '\nHessian eigenvalues:'
H = np.dot(J.T, J)
print H.shape
print np.linalg.eigvals(H)
# Output to file
write_polynomials(cost, out+'/cost.txt')
write_polynomials(residuals, out+'/residuals.txt')
write_polynomials(gradients, out+'/gradients.txt')
write_polynomials(jacobians, out+'/jacobians.txt')
write_solution(true_vars, out+'/solution.txt')
def run_position_only_spline_epipolar():
#
# Construct ground truth
#
num_landmarks = 50
num_frames = 4
num_imu_readings = 80
bezier_degree = 4
out = 'out/position_only_bezier3'
print 'Num landmarks:', num_landmarks
print 'Num frames:', num_frames
print 'Num IMU readings:', num_imu_readings
print 'Bezier curve degree:', bezier_degree
if not os.path.isdir(out):
os.mkdir(out)
# Both splines should start at 0,0,0
frame_times = np.linspace(0, .9, num_frames)
imu_times = np.linspace(0, 1, num_imu_readings)
true_rot_controls = np.random.rand(bezier_degree-1, 3)
true_pos_controls = np.random.rand(bezier_degree-1, 3)
true_landmarks = np.random.randn(num_landmarks, 3)
true_positions = np.array([zero_offset_bezier(true_pos_controls, t) for t in frame_times])
true_cayleys = np.array([zero_offset_bezier(true_rot_controls, t) for t in frame_times])
true_rotations = map(cayley, true_cayleys)
true_imu_cayleys = np.array([zero_offset_bezier(true_rot_controls, t) for t in imu_times])
true_imu_rotations = map(cayley, true_imu_cayleys)
true_gravity = normalized(np.random.rand(3)) * 9.8
true_accel_bias = np.random.rand(3)
true_global_accels = np.array([zero_offset_bezier_second_deriv(true_pos_controls, t) for t in imu_times])
true_accels = [np.dot(R, a + true_gravity) + true_accel_bias
for R, a in zip(true_imu_rotations, true_global_accels)]
true_uprojections = [[np.dot(R, x-p) for x in true_landmarks]
for R, p in zip(true_rotations, true_positions)]
true_projections = [[normalized(zu) for zu in row] for row in true_uprojections]
#
# Construct symbolic versions of the above
#
position_offs = 0
accel_bias_offset = position_offs + (bezier_degree-1)*3
gravity_offset = accel_bias_offset + 3
num_vars = gravity_offset + 3
sym_vars = [Polynomial.coordinate(i, num_vars, Fraction) for i in range(num_vars)]
sym_pos_controls = np.reshape(sym_vars[position_offs:position_offs+(bezier_degree-1)*3], (bezier_degree-1, 3))
sym_accel_bias = np.asarray(sym_vars[accel_bias_offset:accel_bias_offset+3])
sym_gravity = np.asarray(sym_vars[gravity_offset:gravity_offset+3])
true_vars = np.hstack((true_pos_controls.flatten(), true_accel_bias, true_gravity))
assert len(true_vars) == len(sym_vars)
residuals = []
#
# Accel residuals
#
print '\nAccel residuals:'
for i in range(num_imu_readings):
true_R = true_imu_rotations[i]
sym_global_accel = zero_offset_bezier_second_deriv(sym_pos_controls, imu_times[i])
sym_accel = np.dot(true_R, sym_global_accel + sym_gravity) + sym_accel_bias
residual = sym_accel - true_accels[i]
for i in range(3):
print ' Degree of global accel = %d, local accel = %d, residual = %d' % \
(sym_global_accel[i].total_degree, sym_accel[i].total_degree, residual[i].total_degree)
residuals.extend(residual)
#
# Epipolar residuals
#
p0 = np.zeros(3)
R0 = np.eye(3)
for i in range(1, num_frames):
true_s = true_cayleys[i]
true_R = cayley_mat(true_s)
sym_p = zero_offset_bezier(sym_pos_controls, frame_times[i])
sym_E = essential_matrix(R0, p0, true_R, sym_p)
for j in range(num_landmarks):
z = true_projections[i][j]
z0 = true_projections[0][j]
residual = np.dot(z, np.dot(sym_E, z0))
residuals.append(residual)
print '\nNum vars:', num_vars
print 'Num residuals:', len(residuals)
print '\nResiduals:', len(residuals)
cost = Polynomial(num_vars)
for r in residuals:
cost += r*r
print ' %f (degree=%d, length=%d)' % (r(*true_vars), r.total_degree, len(r))
print '\nCost:'
print ' Num terms: %d' % len(cost)
print ' Degree: %d' % cost.total_degree
for term in cost:
print ' ',term
print '\nGradients:'
gradients = cost.partial_derivatives()
for gradient in gradients:
print ' %d (degree=%d, length=%d)' % (gradient(*true_vars), gradient.total_degree, len(gradient))
jacobians = np.array([r.partial_derivatives() for r in residuals])
J = evaluate_array(jacobians, *true_vars)
U, S, V = np.linalg.svd(J)
print '\nJacobian singular values:'
print J.shape
print S
print '\nHessian eigenvalues:'
H = np.dot(J.T, J)
print H.shape
print np.linalg.eigvals(H)
null_space_dims = sum(np.abs(S) < 1e-5)
print '\nNull space dimensions:', null_space_dims
if null_space_dims > 0:
for i in range(null_space_dims):
print ' ',V[-i]
null_monomial = (0,) * num_vars
coordinate_monomials = [list(var.monomials)[0] for var in sym_vars]
A, _ = matrix_form(gradients, coordinate_monomials)
b, _ = matrix_form(gradients, [null_monomial])
b = np.squeeze(b)
AA, bb, kk = quadratic_form(cost)
estimated_vars = np.squeeze(numpy.linalg.solve(AA*2, -b))
print '\nEstimated:'
print estimated_vars
print '\nGround truth:'
print true_vars
print '\nError:'
print np.linalg.norm(estimated_vars - true_vars)
# Output to file
write_polynomials(cost, out+'/cost.txt')
write_polynomials(residuals, out+'/residuals.txt')
write_polynomials(gradients, out+'/gradients.txt')
write_polynomials(jacobians.flat, out+'/jacobians.txt')
write_solution(true_vars, out+'/solution.txt')
def run_sfm():
# Construct symbolic problem
num_landmarks = 4
num_frames = 2
print 'Num observations: ', num_landmarks * num_frames * 2
print 'Num vars: ', num_frames*6 + num_landmarks*3 + num_frames*num_landmarks
true_landmarks = np.random.randn(num_landmarks, 3)
true_positions = np.random.rand(num_frames, 3)
true_cayleys = np.random.rand(num_frames, 3)
true_qs = map(cayley_mat, true_cayleys)
true_betas = map(cayley_denom, true_cayleys)
true_rotations = [(q/b) for (q,b) in zip(true_qs, true_betas)]
true_uprojections = [[np.dot(R, x-p) for x in true_landmarks]
for R,p in zip(true_rotations, true_positions)]
true_projections = [[normalized(zu) for zu in row] for row in true_uprojections]
true_alphas = [[np.linalg.norm(zu) for zu in row] for row in true_uprojections]
true_vars = np.hstack((true_cayleys.flatten(),
true_positions.flatten(),
true_landmarks.flatten(),
np.asarray(true_alphas).flatten()))
#true_projection_mat = np.reshape(true_projections, (num_frames, num_landmarks, 2))
for i in range(num_frames):
p = true_positions[i]
q = true_qs[i]
beta = true_betas[i]
for j in range(num_landmarks):
x = true_landmarks[j]
z = true_projections[i][j]
alpha = true_alphas[i][j]
print alpha * beta * z - np.dot(q, x-p)
# construct symbolic versions of the above
s_offs = 0
p_offs = s_offs + num_frames*3
x_offs = p_offs + num_frames*3
a_offs = x_offs + num_landmarks*3
num_vars = a_offs + num_landmarks*num_frames
sym_vars = [Polynomial.coordinate(i, num_vars, Fraction) for i in range(num_vars)]
sym_cayleys = np.reshape(sym_vars[s_offs:s_offs+num_frames*3], (num_frames, 3))
sym_positions = np.reshape(sym_vars[p_offs:p_offs+num_frames*3], (num_frames, 3))
sym_landmarks = np.reshape(sym_vars[x_offs:x_offs+num_landmarks*3], (num_landmarks, 3))
sym_alphas = np.reshape(sym_vars[a_offs:], (num_frames, num_landmarks))
residuals = []
for i in range(num_frames):
sym_p = sym_positions[i]
sym_s = sym_cayleys[i]
for j in range(num_landmarks):
sym_x = sym_landmarks[j]
sym_a = sym_alphas[i,j]
true_z = true_projections[i][j]
residual = np.dot(cayley_mat(sym_s), sym_x-sym_p) - sym_a * cayley_denom(sym_s) * true_z
residuals.extend(residual)
print 'Residuals:'
cost = Polynomial(num_vars)
for residual in residuals:
cost += np.dot(residual, residual)
print ' ',residual(*true_vars) #ri.num_vars, len(true_vars)
print '\nGradients:'
gradient = [cost.partial_derivative(i) for i in range(num_vars)]
for gi in gradient:
print gi(*true_vars)
j = np.array([[r.partial_derivative(i)(*true_vars) for i in range(num_vars)]
for r in residuals])
print '\nJacobian singular values:'
print j.shape
u, s, v = np.linalg.svd(j)
print s
print '\nHessian eigenvalues:'
h = np.dot(j.T, j)
print h.shape
print np.linalg.eigvals(h)
def run_epipolar():
# Construct symbolic problem
num_landmarks = 10
num_frames = 3
true_landmarks = np.random.randn(num_landmarks, 3)
true_positions = np.vstack((np.zeros(3),
np.random.rand(num_frames-1, 3)))
true_cayleys = np.vstack((np.zeros(3),
np.random.rand(num_frames-1, 3)))
true_qs = map(cayley_mat, true_cayleys)
true_rotations = map(cayley, true_cayleys)
true_uprojections = [[np.dot(R, x-p) for x in true_landmarks]
for R,p in zip(true_rotations, true_positions)]
true_projections = [[normalized(zu) for zu in row] for row in true_uprojections]
p0 = true_positions[0]
q0 = true_qs[0]
for i in range(1, num_frames):
p = true_positions[i]
q = true_qs[i]
E = essential_matrix(q0, p0, q, p)
for j in range(num_landmarks):
z = true_projections[i][j]
z0 = true_projections[0][j]
print np.dot(z, np.dot(E, z0))
# construct symbolic versions of the above
s_offs = 0
p_offs = s_offs + (num_frames-1)*3
num_vars = p_offs + (num_frames-1)*3
sym_vars = [Polynomial.coordinate(i, num_vars, Fraction) for i in range(num_vars)]
sym_cayleys = np.reshape(sym_vars[s_offs:s_offs+(num_frames-1)*3], (num_frames-1, 3))
sym_positions = np.reshape(sym_vars[p_offs:p_offs+(num_frames-1)*3], (num_frames-1, 3))
true_vars = np.hstack((true_cayleys[1:].flatten(),
true_positions[1:].flatten()))
residuals = []
p0 = np.zeros(3)
R0 = np.eye(3)
for i in range(1, num_frames):
sym_p = sym_positions[i-1]
sym_s = sym_cayleys[i-1]
sym_q = cayley_mat(sym_s)
sym_E = essential_matrix(R0, p0, sym_q, sym_p)
for j in range(num_landmarks):
z = true_projections[i][j]
z0 = true_projections[0][j]
residual = np.dot(z, np.dot(sym_E, z0))
print 'Residual poly: ',len(residual), residual.total_degree
residuals.append(residual)
print 'Num sym_vars:',num_vars
print 'Num residuals:',len(residuals)
print 'Residuals:', len(residuals)
cost = Polynomial(num_vars)
for residual in residuals:
#cost += np.dot(residual, residual)
print ' ',residual(*true_vars) #ri.num_vars, len(true_vars)
print '\nGradients:'
gradient = [cost.partial_derivative(i) for i in range(num_vars)]
for gi in gradient:
print ' ',gi(*true_vars)
J = np.array([[r.partial_derivative(i)(*true_vars) for i in range(num_vars)]
for r in residuals])
print '\nJacobian singular values:'
print J.shape
U,S,V = np.linalg.svd(J)
print S
print V[-1]
print V[-2]
print '\nHessian eigenvalues:'
H = np.dot(J.T, J)
print H.shape
print np.linalg.eigvals(H)
def main():
np.random.seed(1)
#
# Construct ground truth
#
num_frames = 5
num_landmarks = 10
num_imu_readings = 8
bezier_degree = 3
out = 'out/full_initialization'
print 'Num landmarks:', num_landmarks
print 'Num frames:', num_frames
print 'Num IMU readings:', num_imu_readings
print 'Bezier curve degree:', bezier_degree
if not os.path.isdir(out):
os.mkdir(out)
# Both splines should start at 0,0,0
frame_times = np.linspace(0, .9, num_frames)
accel_times = np.linspace(0, 1, num_imu_readings)
true_pos_controls = np.random.randn(bezier_degree-1, 3)
true_orient_controls = np.random.randn(bezier_degree-1, 3)
true_landmarks = np.random.randn(num_landmarks, 3)
true_frame_positions = np.array([zero_offset_bezier(true_pos_controls, t) for t in frame_times])
true_frame_cayleys = np.array([zero_offset_bezier(true_orient_controls, t) for t in frame_times])
true_frame_orientations = np.array(map(cayley, true_frame_cayleys))
true_imu_cayleys = np.array([zero_offset_bezier(true_orient_controls, t) for t in accel_times])
true_imu_orientations = np.array(map(cayley, true_imu_cayleys))
true_gravity_magnitude = 9.8
true_gravity = normalized(np.random.rand(3)) * true_gravity_magnitude
true_accel_bias = np.random.randn(3)
true_global_accels = np.array([zero_offset_bezier_second_deriv(true_pos_controls, t) for t in accel_times])
true_accels = np.array([np.dot(R, a + true_gravity) + true_accel_bias
for R, a in zip(true_imu_orientations, true_global_accels)])
true_features = np.array([[normalized(np.dot(R, x-p)) for x in true_landmarks]
for R, p in zip(true_frame_orientations, true_frame_positions)])
true_vars = np.hstack((true_pos_controls.flatten(),
true_orient_controls.flatten(),
true_accel_bias,
true_gravity))
print np.min(true_features.reshape((-1, 3)), axis=0)
print np.max(true_features.reshape((-1, 3)), axis=0)
#
# Add sensor noise
#
accel_noise = 0
feature_noise = 0
observed_features = true_features.copy()
observed_accels = true_accels.copy()
if accel_noise > 0:
observed_accels += np.random.randn(*observed_accels.shape) * accel_noise
if feature_noise > 0:
observed_features += np.random.rand(*observed_features.shape) * feature_noise
#
# Construct symbolic versions of the above
#
num_position_vars = (bezier_degree-1)*3
num_orientation_vars = (bezier_degree-1)*3
num_accel_bias_vars = 3
num_gravity_vars = 3
block_sizes = [num_position_vars, num_orientation_vars, num_accel_bias_vars, num_gravity_vars]
num_vars = sum(block_sizes)
sym_vars = [Polynomial.coordinate(i, num_vars, Fraction) for i in range(num_vars)]
sym_pos_controls, sym_orient_controls, sym_accel_bias, sym_gravity = map(np.array, chop(sym_vars, block_sizes))
sym_pos_controls = sym_pos_controls.reshape((-1, 3))
sym_orient_controls = sym_orient_controls.reshape((-1, 3))
assert len(true_vars) == len(sym_vars)
#
# Accel residuals
#
residuals = []
print 'Accel residuals:'
for i, t in enumerate(accel_times):
sym_cayley = zero_offset_bezier(sym_orient_controls, t)
sym_orient = cayley_mat(sym_cayley)
sym_denom = cayley_denom(sym_cayley)
sym_global_accel = zero_offset_bezier_second_deriv(sym_pos_controls, t)
sym_accel = np.dot(sym_orient, sym_global_accel + sym_gravity) + sym_denom * sym_accel_bias
residual = sym_accel - sym_denom * observed_accels[i]
residuals.extend(residual)
for r in residual:
print ' %f (degree=%d, length=%d)' % (r(*true_vars), r.total_degree, len(r))
#
# Epipolar residuals
#
print 'Epipolar residuals:'
for i, ti in enumerate(frame_times):
if i == 0: continue
sym_Ri = cayley_mat(zero_offset_bezier(sym_orient_controls, ti))
sym_pi = zero_offset_bezier(sym_pos_controls, ti)
sym_E = essential_matrix_from_relative_pose(sym_Ri, sym_pi)
for k in range(num_landmarks):
z1 = observed_features[0][k]
zi = observed_features[i][k]
residual = np.dot(zi, np.dot(sym_E, z1))
residuals.append(residual)
r = residual
print ' %f (degree=%d, length=%d)' % (r(*true_vars), r.total_degree, len(r))
#
# Construct cost
#
cost = Polynomial(num_vars)
for r in residuals:
cost += r*r
gradients = cost.partial_derivatives()
print '\nNum vars:', num_vars
print 'Num residuals:', len(residuals)
print '\nCost:'
print ' Num terms: %d' % len(cost)
print ' Degree: %d' % cost.total_degree
#
# Output to file
#
write_polynomials(cost, out+'/cost.txt')
write_polynomials(residuals, out+'/residuals.txt')
write_polynomials(gradients, out+'/gradients.txt')
write_solution(true_vars, out+'/solution.txt')
np.savetxt(out+'/feature_measurements.txt', observed_features.reshape((-1, 3)))
np.savetxt(out+'/accel_measurements.txt', observed_accels)
np.savetxt(out+'/problem_size.txt', [num_frames, num_landmarks, num_imu_readings])
np.savetxt(out+'/frame_times.txt', frame_times)
np.savetxt(out+'/accel_times.txt', accel_times)
np.savetxt(out+'/true_pos_controls.txt', true_pos_controls)
np.savetxt(out+'/true_orient_controls.txt', true_orient_controls)
np.savetxt(out+'/true_accel_bias.txt', true_accel_bias)
np.savetxt(out+'/true_gravity.txt', true_gravity)
return
#
# Plot
#
fig = plt.figure(figsize=(14,6))
ax = fig.add_subplot(1, 2, 1, projection='3d')
ts = np.linspace(0, 1, 100)
true_ps = np.array([zero_offset_bezier(true_pos_controls, t) for t in ts])
ax.plot(true_ps[:, 0], true_ps[:, 1], true_ps[:, 2], '-b')
plt.show()
def run_from_data():
#
# Load data
#
path = Path('/Users/alexflint/Code/spline-initialization/out')
frame_orientation_data = np.loadtxt(str(path / 'frame_orientations.txt'))
frame_timestamps = frame_orientation_data[:, 0]
frame_orientations = frame_orientation_data[:, 1:].reshape((-1, 3, 3))
accel_data = np.loadtxt(str(path / 'accelerometer.txt'))
accel_timestamps = accel_data[:, 0]
accel_readings = accel_data[:, 1:]
accel_orientation_data = np.loadtxt(str(path / 'accel_orientations.txt'))
accel_orientations = accel_orientation_data[:, 1:].reshape((-1, 3, 3))
feature_data = np.loadtxt(str(path / 'features.txt'))
landmarks_ids = sorted(set(feature_data[:, 0].astype(int)))
frame_ids = sorted(set(feature_data[:, 1].astype(int)))
landmark_index_by_id = {idx: i for i, idx in enumerate(landmarks_ids)}
frame_index_by_id = {idx: i for i, idx in enumerate(frame_ids)}
assert len(accel_orientations) == len(accel_readings)
assert len(frame_ids) == len(frame_orientations) == len(frame_timestamps)
num_frames = len(frame_ids)
num_landmarks = len(landmarks_ids)
num_imu_readings = len(accel_readings)
bezier_degree = 4
print 'Num landmarks:', num_landmarks
print 'Num frames:', num_frames
print 'Num IMU readings:', num_imu_readings
print 'Bezier curve degree:', bezier_degree
#
# Make feature table
#
features = np.ones((num_frames, num_landmarks, 2))
features.fill(np.nan)
for landmark_id, frame_id, feature in zip(landmarks_ids, frame_ids, feature_data[:, 2:]):
i = frame_index_by_id[frame_id]
j = landmark_index_by_id[landmark_id]
features[i, j] = feature
feature_mask[i, j] = True
#
# Normalize timestamps to [0,1]
#
begin_time = min(np.min(accel_timestamps), np.min(frame_timestamps))
end_time = max(np.max(accel_timestamps), np.max(frame_timestamps))
accel_timestamps = (accel_timestamps - begin_time) / (end_time - begin_time)
frame_timestamps = (frame_timestamps - begin_time) / (end_time - begin_time)
#
# Construct symbolic versions of the above
#
position_offs = 0
accel_bias_offset = position_offs + (bezier_degree-1)*3
gravity_offset = accel_bias_offset + 3
num_vars = gravity_offset + 3
sym_vars = [Polynomial.coordinate(i, num_vars, Fraction) for i in range(num_vars)]
sym_pos_controls = np.reshape(sym_vars[position_offs:position_offs+(bezier_degree-1)*3], (bezier_degree-1, 3))
sym_accel_bias = np.asarray(sym_vars[accel_bias_offset:accel_bias_offset+3])
sym_gravity = np.asarray(sym_vars[gravity_offset:gravity_offset+3])
#
# Compute residuals
#
epipolar_residuals = evaluate_epipolar_residuals(sym_pos_controls, frame_timestamps,
frame_orientations, features, feature_mask)
accel_residuals = evaluate_accel_residuals(sym_pos_controls, sym_accel_bias, sym_gravity,
accel_timestamps, accel_readings, accel_orientations)
residuals = accel_residuals + epipolar_residuals
print '\nNum vars:', num_vars
print 'Num residuals:', len(residuals)
print '\nResiduals:', len(residuals)
cost = Polynomial(num_vars)
for r in residuals:
cost += r*r
print ' degree=%d, length=%d' % (r.total_degree, len(r))
print '\nCost:'
print ' Num terms: %d' % len(cost)
print ' Degree: %d' % cost.total_degree
# Solve
A, b, k = quadratic_form(cost)
estimated_vars = np.squeeze(np.linalg.solve(A*2, -b))
estimated_pos_controls = np.reshape(estimated_vars[position_offs:position_offs+(bezier_degree-1)*3], (bezier_degree-1, 3))
estimated_positions = np.array([bezier.zero_offset_bezier(estimated_pos_controls, t) for t in frame_timestamps])
estimated_accel_bias = np.asarray(estimated_vars[accel_bias_offset:accel_bias_offset+3])
estimated_gravity = np.asarray(estimated_vars[gravity_offset:gravity_offset+3])
print '\nEstimated:'
print estimated_vars
print 'Estimated accel bias:', estimated_accel_bias
print 'Estimated gravity:', estimated_gravity
print 'Estimated gravity magnitude:', np.linalg.norm(estimated_gravity)
fig = plt.figure(figsize=(14,6))
ax = fig.add_subplot(1, 2, 1, projection='3d')
ts = np.linspace(0, 1, 100)
estimated_ps = np.array([bezier.zero_offset_bezier(estimated_pos_controls, t) for t in ts])
ax.plot(estimated_ps[:, 0], estimated_ps[:, 1], estimated_ps[:, 2], '-r')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.show()
def run_simulation():
np.random.seed(1)
#
# Construct ground truth
#
num_frames = 5
num_landmarks = 50
num_imu_readings = 80
bezier_degree = 4
out = 'out/position_only_bezier3'
print 'Num landmarks:', num_landmarks
print 'Num frames:', num_frames
print 'Num IMU readings:', num_imu_readings
print 'Bezier curve degree:', bezier_degree
if not os.path.isdir(out):
os.mkdir(out)
# Both splines should start at 0,0,0
frame_times = np.linspace(0, .9, num_frames)
imu_times = np.linspace(0, 1, num_imu_readings)
true_rot_controls = np.random.randn(bezier_degree-1, 3)
true_pos_controls = np.random.randn(bezier_degree-1, 3)
true_landmarks = np.random.randn(num_landmarks, 3)
true_frame_cayleys = np.array([bezier.zero_offset_bezier(true_rot_controls, t) for t in frame_times])
true_frame_orientations = np.array(map(cayley, true_frame_cayleys))
true_frame_positions = np.array([bezier.zero_offset_bezier(true_pos_controls, t) for t in frame_times])
true_imu_cayleys = np.array([bezier.zero_offset_bezier(true_rot_controls, t) for t in imu_times])
true_imu_orientations = np.array(map(cayley, true_imu_cayleys))
true_gravity_magnitude = 9.8
true_gravity = normalized(np.random.rand(3)) * true_gravity_magnitude
true_accel_bias = np.random.randn(3)
true_global_accels = np.array([bezier.zero_offset_bezier_second_deriv(true_pos_controls, t) for t in imu_times])
true_accels = np.array([np.dot(R, a + true_gravity) + true_accel_bias
for R, a in zip(true_imu_orientations, true_global_accels)])
true_features = np.array([[normalized(np.dot(R, x-p)) for x in true_landmarks]
for R, p in zip(true_frame_orientations, true_frame_positions)])
print np.min(true_features.reshape((-1, 3)), axis=0)
print np.max(true_features.reshape((-1, 3)), axis=0)
#
# Add sensor noise
#
accel_noise = 0#0.001
feature_noise = 0#0.01
orientation_noise = 0.01
observed_frame_orientations = true_frame_orientations.copy()
observed_imu_orientations = true_imu_orientations.copy()
observed_features = true_features.copy()
observed_accels = true_accels.copy()
if orientation_noise > 0:
for i, R in enumerate(observed_frame_orientations):
R_noise = SO3.exp(np.random.randn(3)*orientation_noise)
observed_frame_orientations[i] = np.dot(R_noise, R)
for i, R in enumerate(observed_imu_orientations):
R_noise = SO3.exp(np.random.randn(3)*orientation_noise)
observed_imu_orientations[i] = np.dot(R_noise, R)
if accel_noise > 0:
observed_accels += np.random.randn(*observed_accels.shape) * accel_noise
if feature_noise > 0:
observed_features += np.random.rand(*observed_features.shape) * feature_noise
#
# Construct symbolic versions of the above
#
position_offs = 0
accel_bias_offset = position_offs + (bezier_degree-1)*3
gravity_offset = accel_bias_offset + 3
num_vars = gravity_offset + 3
sym_vars = [Polynomial.coordinate(i, num_vars, Fraction) for i in range(num_vars)]
sym_pos_controls = np.reshape(sym_vars[position_offs:position_offs+(bezier_degree-1)*3], (bezier_degree-1, 3))
sym_accel_bias = np.asarray(sym_vars[accel_bias_offset:accel_bias_offset+3])
sym_gravity = np.asarray(sym_vars[gravity_offset:gravity_offset+3])
true_vars = np.hstack((true_pos_controls.flatten(), true_accel_bias, true_gravity))
assert len(true_vars) == len(sym_vars)
#
# Compute residuals
#
epipolar_residuals = evaluate_epipolar_residuals(sym_pos_controls, frame_times,
observed_frame_orientations, observed_features)
accel_residuals = evaluate_accel_residuals(sym_pos_controls, sym_accel_bias, sym_gravity,
imu_times, observed_accels, observed_imu_orientations)
residuals = np.hstack((accel_residuals, epipolar_residuals))
print '\nNum vars:', num_vars
print 'Num residuals:', len(residuals)
print '\nResiduals:', len(residuals)
cost = Polynomial(num_vars)
for r in residuals:
cost += r*r
print ' %f (degree=%d, length=%d)' % (r(*true_vars), r.total_degree, len(r))
print '\nCost:'
print ' Num terms: %d' % len(cost)
print ' Degree: %d' % cost.total_degree
# Solve
A, b, k = quadratic_form(cost)
estimated_vars = np.squeeze(np.linalg.solve(A*2, -b))
estimated_pos_controls = np.reshape(estimated_vars[position_offs:position_offs+(bezier_degree-1)*3], (bezier_degree-1, 3))
estimated_positions = np.array([bezier.zero_offset_bezier(estimated_pos_controls, t) for t in frame_times])
estimated_accel_bias = np.asarray(estimated_vars[accel_bias_offset:accel_bias_offset+3])
estimated_gravity = np.asarray(estimated_vars[gravity_offset:gravity_offset+3])
re_estimated_gravity = normalized(estimated_gravity) * true_gravity_magnitude
print '\nEstimated:'
print estimated_vars
print '\nGround truth:'
print true_vars
print '\nTotal Error:', np.linalg.norm(estimated_vars - true_vars)
print 'Accel bias error:', np.linalg.norm(estimated_accel_bias - true_accel_bias)
print 'Gravity error:', np.linalg.norm(estimated_gravity - true_gravity)
print ' True gravity:', true_gravity
print ' Estimated gravity:', estimated_gravity
print ' Estimated gravity magnitude:', np.linalg.norm(estimated_gravity)
print ' Re-normalized gravity error: ', np.linalg.norm(re_estimated_gravity - true_gravity)
for i in range(num_frames):
print 'Frame %d error: %f' % (i, np.linalg.norm(estimated_positions[i] - true_frame_positions[i]))
fig = plt.figure(figsize=(14,6))
ax = fig.add_subplot(1, 2, 1, projection='3d')
ts = np.linspace(0, 1, 100)
true_ps = np.array([bezier.zero_offset_bezier(true_pos_controls, t) for t in ts])
estimated_ps = np.array([bezier.zero_offset_bezier(estimated_pos_controls, t) for t in ts])
ax.plot(true_ps[:, 0], true_ps[:, 1], true_ps[:, 2], '-b')
ax.plot(estimated_ps[:, 0], estimated_ps[:, 1], estimated_ps[:, 2], '-r')
plt.show()
