def forward_with_rotation_matrices_mask(self, xs, hat_xs):
"""Forward errors with rotation matrices"""
N = xs.shape[0]
masks = xs[:, :, 3].unsqueeze(1)
masks = torch.nn.functional.conv1d(
masks, self.weight, bias=None,
stride=self.min_train_freq).double().transpose(1, 2)
masks[masks < 1] = 0
Xs = SO3.exp(xs[:, ::self.min_train_freq, :3].reshape(-1, 3).double())
hat_xs = self.dt * hat_xs.reshape(-1, 3).double()
Omegas = SO3.exp(hat_xs[:, :3])
# compute increment at min_train_freq by decimation
for k in range(self.min_N):
Omegas = Omegas[::2].bmm(Omegas[1::2])
rs = SO3.log(bmtm(Omegas, Xs)).reshape(N, -1, 3)[:, self.N0:]
loss = self.f_huber(rs)
# compute increment from min_train_freq to max_train_freq
for k in range(self.min_N, self.max_N):
Omegas = Omegas[::2].bmm(Omegas[1::2])
Xs = Xs[::2].bmm(Xs[1::2])
masks = masks[:, ::2] * masks[:, 1::2]
rs = SO3.log(bmtm(Omegas, Xs)).reshape(N, -1, 3)[:, self.N0:]
rs = rs[masks[:, self.N0:].squeeze(2) == 1]
loss = loss + self.f_huber(rs[:, 2]) / (2**(k - self.min_N + 1))
return loss
def plot_orientation_error(self, imu_Rots, net_Rots, N):
gt = self.gt['Rots'][:N].cuda()
raw_err = 180/np.pi*SO3.log(bmtm(imu_Rots, gt)).cpu()
net_err = 180/np.pi*SO3.log(bmtm(net_Rots, gt)).cpu()
title = "$SO(3)$ orientation error"
fig, axs = plt.subplots(3, 1, sharex=True, figsize=self.figsize)
axs[0].set(ylabel='roll (deg)', title=title)
axs[1].set(ylabel='pitch (deg)')
axs[2].set(xlabel='$t$ (min)', ylabel='yaw (deg)')
for i in range(3):
axs[i].plot(self.ts, raw_err[:, i], color='red', label=r'raw IMU')
axs[i].plot(self.ts, net_err[:, i], color='blue', label=r'net IMU')
axs[i].set_ylim(-10, 10)
axs[i].set_xlim(self.ts[0], self.ts[-1])
self.savefig(axs, fig, 'orientation_error')
def forward_with_rotation_matrices(self, xs, hat_xs):
"""Forward errors with rotation matrices"""
N = xs.shape[0]
Xs = SO3.exp(xs[:, ::self.min_train_freq].reshape(-1, 3).double())
hat_xs = self.dt * hat_xs.reshape(-1, 3).double()
Omegas = SO3.exp(hat_xs[:, :3])
# compute increment at min_train_freq by decimation
for k in range(self.min_N):
Omegas = Omegas[::2].bmm(Omegas[1::2])
rs = SO3.log(bmtm(Omegas, Xs)).reshape(N, -1, 3)[:, self.N0:]
loss = self.f_huber(rs)
# compute increment from min_train_freq to max_train_freq
for k in range(self.min_N, self.max_N):
Omegas = Omegas[::2].bmm(Omegas[1::2])
Xs = Xs[::2].bmm(Xs[1::2])
rs = SO3.log(bmtm(Omegas, Xs)).reshape(N, -1, 3)[:, self.N0:]
loss = loss + self.f_huber(rs) / (2**(k - self.min_N + 1))
return loss
def plot_orientation_err(self):
title = "Position error as function of time " + self.end_title
err = SO3.log(bmtm(self.gt['Rots'].cuda(),
self.iekf['Rots'].cuda())).cpu()
fig, axs = plt.subplots(3, 1, sharex=True, figsize=self.figsize)
axs[0].set(ylabel='roll (deg)', title=title)
axs[1].set(ylabel='pitch (deg)')
axs[2].set(xlabel='$t$ (min)', ylabel='yaw (deg)')
for i in range(3):
axs[i].plot(self.ts, err[:, i], color="blue")
axs[i].set_xlim(self.ts[0], self.ts[-1])
self.savefig(axs, fig, 'orientation_error')
def read_data(self, data_dir):
r"""Read the data from the dataset"""
f = os.path.join(self.predata_dir, 'dataset-room1_512_16_gt.p')
if True and os.path.exists(f):
return
print("Start read_data, be patient please")
def set_path(seq):
path_imu = os.path.join(data_dir, seq, "mav0", "imu0", "data.csv")
path_gt = os.path.join(data_dir, seq, "mav0", "mocap0", "data.csv")
return path_imu, path_gt
sequences = os.listdir(data_dir)
# read each sequence
for sequence in sequences:
print("\nSequence name: " + sequence)
if 'room' not in sequence:
continue
path_imu, path_gt = set_path(sequence)
imu = np.genfromtxt(path_imu, delimiter=",", skip_header=1)
gt = np.genfromtxt(path_gt, delimiter=",", skip_header=1)
# time synchronization between IMU and ground truth
t0 = np.max([gt[0, 0], imu[0, 0]])
t_end = np.min([gt[-1, 0], imu[-1, 0]])
# start index
idx0_imu = np.searchsorted(imu[:, 0], t0)
idx0_gt = np.searchsorted(gt[:, 0], t0)
# end index
idx_end_imu = np.searchsorted(imu[:, 0], t_end, 'right')
idx_end_gt = np.searchsorted(gt[:, 0], t_end, 'right')
# subsample
imu = imu[idx0_imu:idx_end_imu]
gt = gt[idx0_gt:idx_end_gt]
ts = imu[:, 0] / 1e9
# interpolate
t_gt = gt[:, 0] / 1e9
gt = self.interpolate(gt, t_gt, ts)
# take ground truth position
p_gt = gt[:, 1:4]
p_gt = p_gt - p_gt[0]
# take ground true quaternion pose
q_gt = SO3.qnorm(torch.Tensor(gt[:, 4:8]).double())
Rot_gt = SO3.from_quaternion(q_gt.cuda(), ordering='wxyz').cpu()
# convert from numpy
p_gt = torch.Tensor(p_gt).double()
v_gt = torch.zeros_like(p_gt).double()
v_gt[1:] = (p_gt[1:] - p_gt[:-1]) / self.dt
imu = torch.Tensor(imu[:, 1:]).double()
# compute pre-integration factors for all training
mtf = self.min_train_freq
dRot_ij = bmtm(Rot_gt[:-mtf], Rot_gt[mtf:])
dRot_ij = SO3.dnormalize(dRot_ij.cuda())
dxi_ij = SO3.log(dRot_ij).cpu()
# masks with 1 when ground truth is available, 0 otherwise
masks = dxi_ij.new_ones(dxi_ij.shape[0])
tmp = np.searchsorted(t_gt, ts[:-mtf])
diff_t = ts[:-mtf] - t_gt[tmp]
masks[np.abs(diff_t) > 0.01] = 0
# save all the sequence
mondict = {
'xs': torch.cat((dxi_ij, masks.unsqueeze(1)), 1).float(),
'us': imu.float(),
}
pdump(mondict, self.predata_dir, sequence + ".p")
# save ground truth
mondict = {
'ts': ts,
'qs': q_gt.float(),
'vs': v_gt.float(),
'ps': p_gt.float(),
}
pdump(mondict, self.predata_dir, sequence + "_gt.p")
def read_data(self, data_dir):
r"""Read the data from the dataset"""
f = os.path.join(self.predata_dir, 'MH_01_easy.p')
if True and os.path.exists(f):
return
print("Start read_data, be patient please")
def set_path(seq):
path_imu = os.path.join(data_dir, seq, "mav0", "imu0", "data.csv")
path_gt = os.path.join(data_dir, seq, "mav0",
"state_groundtruth_estimate0", "data.csv")
return path_imu, path_gt
sequences = os.listdir(data_dir)
# read each sequence
for sequence in sequences:
print("\nSequence name: " + sequence)
path_imu, path_gt = set_path(sequence)
imu = np.genfromtxt(path_imu, delimiter=",", skip_header=1)
gt = np.genfromtxt(path_gt, delimiter=",", skip_header=1)
# time synchronization between IMU and ground truth
t0 = np.max([gt[0, 0], imu[0, 0]])
t_end = np.min([gt[-1, 0], imu[-1, 0]])
# start index
idx0_imu = np.searchsorted(imu[:, 0], t0)
idx0_gt = np.searchsorted(gt[:, 0], t0)
# end index
idx_end_imu = np.searchsorted(imu[:, 0], t_end, 'right')
idx_end_gt = np.searchsorted(gt[:, 0], t_end, 'right')
# subsample
imu = imu[idx0_imu:idx_end_imu]
gt = gt[idx0_gt:idx_end_gt]
ts = imu[:, 0] / 1e9
# interpolate
gt = self.interpolate(gt, gt[:, 0] / 1e9, ts)
# take ground truth position
p_gt = gt[:, 1:4]
p_gt = p_gt - p_gt[0]
# take ground true quaternion pose
q_gt = torch.Tensor(gt[:, 4:8]).double()
q_gt = q_gt / q_gt.norm(dim=1, keepdim=True)
Rot_gt = SO3.from_quaternion(q_gt.cuda(), ordering='wxyz').cpu()
# convert from numpy
p_gt = torch.Tensor(p_gt).double()
v_gt = torch.tensor(gt[:, 8:11]).double()
imu = torch.Tensor(imu[:, 1:]).double()
# compute pre-integration factors for all training
mtf = self.min_train_freq
dRot_ij = bmtm(Rot_gt[:-mtf], Rot_gt[mtf:])
dRot_ij = SO3.dnormalize(dRot_ij.cuda())
dxi_ij = SO3.log(dRot_ij).cpu()
# save for all training
mondict = {
'xs': dxi_ij.float(),
'us': imu.float(),
}
pdump(mondict, self.predata_dir, sequence + ".p")
# save ground truth
mondict = {
'ts': ts,
'qs': q_gt.float(),
'vs': v_gt.float(),
'ps': p_gt.float(),
}
pdump(mondict, self.predata_dir, sequence + "_gt.p")