def project(poses, ecef_pos):
img_positions = np.zeros((len(poses), 2))
for i, p in enumerate(poses):
cam_frame = orient.rotations_from_quats(p[3:]).T.dot(ecef_pos - p[:3])
img_positions[i] = np.array(
[cam_frame[1] / cam_frame[0], cam_frame[2] / cam_frame[0]])
return img_positions
def device_from_ecef(pos_ecef, orientation_ecef, pt_ecef):
# device from ecef frame
# device frame is x -> forward, y-> right, z -> down
# accepts single pt or array of pts
input_shape = pt_ecef.shape
pt_ecef = np.atleast_2d(pt_ecef)
ecef_from_device_rot = orient.rotations_from_quats(orientation_ecef)
device_from_ecef_rot = ecef_from_device_rot.T
pt_ecef_rel = pt_ecef - pos_ecef
pt_device = np.einsum('jk,ik->ij', device_from_ecef_rot, pt_ecef_rel)
return pt_device.reshape(input_shape)
def compute_pos_python(self, poses, img_positions, check_quality=False):
# This procedure is also described
# in the MSCKF paper (Mourikis et al. 2007)
x = np.array([img_positions[-1][0], img_positions[-1][1], 0.1])
res = opt.leastsq(self.residual,
x,
Dfun=self.residual_jac,
args=(poses, img_positions)) # scipy opt
#res = self.gauss_newton(self.residual, self.residual_jac, x, (poses, img_positions)) # diy gauss_newton
alpha, beta, rho = res[0]
rot_0_to_g = (orient.rotations_from_quats(poses[-1,
3:])).dot(self.to_c.T)
return (rot_0_to_g.dot(np.array([alpha, beta, 1
]))) / rho + poses[-1, :3]
def handle_gps(self, log, current_time):
self.converter = coord.LocalCoord.from_geodetic([
log.gpsLocationExternal.latitude,
log.gpsLocationExternal.longitude, log.gpsLocationExternal.altitude
])
fix_ecef = self.converter.ned2ecef([0, 0, 0])
# initing with bad bearing allowed, maybe bad?
if not self.filter_ready and len(list(self.dog.orbits.keys(
))) > 6: # and log.gpsLocationExternal.speed > 5:
self.filter_ready = True
initial_ecef = fix_ecef
initial_state = np.zeros(29)
gps_bearing = log.gpsLocationExternal.bearing * (np.pi / 180)
initial_pose_ecef = ecef_euler_from_ned(initial_ecef,
[0, 0, gps_bearing])
initial_pose_ecef_quat = euler2quat(initial_pose_ecef)
gps_speed = log.gpsLocationExternal.speed
quat_uncertainty = 0.2**2
initial_pose_ecef_quat = euler2quat(initial_pose_ecef)
initial_state[:3] = initial_ecef
initial_state[3:7] = initial_pose_ecef_quat
initial_state[7:10] = rotations_from_quats(
initial_pose_ecef_quat).dot(np.array([gps_speed, 0, 0]))
initial_state[18] = 1
initial_state[22] = 1
covs_diag = np.array([
10**2, 10**2, 10**2, quat_uncertainty, quat_uncertainty,
quat_uncertainty, 2**2, 2**2, 2**2, 1, 1, 1, 20000000**2,
100**2, 0.01**2, 0.01**2, 0.01**2, 0.02**2, 2**2, 2**2, 2**2,
.01**2, 0.01**2, 0.01**2, 0.01**2, 10**2, 1**2, 0.2**2
])
self.kf.init_state(initial_state,
covs=np.diag(covs_diag),
filter_time=current_time)
print("Filter initialized")
elif self.filter_ready:
#self.update_kalman(current_time, ObservationKind.ECEF_POS, fix_ecef)
gps_est_error = np.sqrt((self.kf.x[0] - fix_ecef[0])**2 +
(self.kf.x[1] - fix_ecef[1])**2 +
(self.kf.x[2] - fix_ecef[2])**2)
if gps_est_error > 50:
cloudlog.info(
"Locationd vs ubloxLocation difference too large, kalman reset"
)
self.reset_kalman()
def handle_gps(self, current_time, log):
converter = coord.LocalCoord.from_geodetic(
[log.latitude, log.longitude, log.altitude])
fix_ecef = converter.ned2ecef([0, 0, 0])
# TODO initing with bad bearing not allowed, maybe not bad?
if not self.filter_ready and log.speed > 5:
self.filter_ready = True
initial_ecef = fix_ecef
gps_bearing = math.radians(log.bearing)
initial_pose_ecef = ecef_euler_from_ned(initial_ecef,
[0, 0, gps_bearing])
initial_pose_ecef_quat = euler2quat(initial_pose_ecef)
gps_speed = log.speed
quat_uncertainty = 0.2**2
initial_pose_ecef_quat = euler2quat(initial_pose_ecef)
initial_state = initial_x
initial_covs_diag = initial_P_diag
initial_state[States.ECEF_POS] = initial_ecef
initial_state[States.ECEF_ORIENTATION] = initial_pose_ecef_quat
initial_state[States.ECEF_VELOCITY] = rotations_from_quats(
initial_pose_ecef_quat).dot(np.array([gps_speed, 0, 0]))
initial_covs_diag[States.ECEF_POS_ERR] = 10**2
initial_covs_diag[States.ECEF_ORIENTATION_ERR] = quat_uncertainty
initial_covs_diag[States.ECEF_VELOCITY_ERR] = 1**2
self.kf.init_state(initial_state,
covs=np.diag(initial_covs_diag),
filter_time=current_time)
cloudlog.info("Filter initialized")
elif self.filter_ready:
self.update_kalman(current_time, ObservationKind.ECEF_POS,
fix_ecef)
gps_est_error = np.sqrt((self.kf.x[0] - fix_ecef[0])**2 +
(self.kf.x[1] - fix_ecef[1])**2 +
(self.kf.x[2] - fix_ecef[2])**2)
if gps_est_error > 50:
cloudlog.error(
"Locationd vs ubloxLocation difference too large, kalman reset"
)
self.reset_kalman()
def liveLocationMsg(self, time):
fix = messaging.log.LiveLocationData.new_message()
predicted_state = self.kf.x
fix_ecef = predicted_state[States.ECEF_POS]
fix_pos_geo = coord.ecef2geodetic(fix_ecef)
fix.lat = float(fix_pos_geo[0])
fix.lon = float(fix_pos_geo[1])
fix.alt = float(fix_pos_geo[2])
fix.speed = float(np.linalg.norm(
predicted_state[States.ECEF_VELOCITY]))
orientation_ned_euler = ned_euler_from_ecef(
fix_ecef, quat2euler(predicted_state[States.ECEF_ORIENTATION]))
fix.roll = math.degrees(orientation_ned_euler[0])
fix.pitch = math.degrees(orientation_ned_euler[1])
fix.heading = math.degrees(orientation_ned_euler[2])
fix.gyro = [
float(predicted_state[10]),
float(predicted_state[11]),
float(predicted_state[12])
]
fix.accel = [
float(predicted_state[19]),
float(predicted_state[20]),
float(predicted_state[21])
]
local_vel = rotations_from_quats(
predicted_state[States.ECEF_ORIENTATION]).T.dot(
predicted_state[States.ECEF_VELOCITY])
fix.pitchCalibration = math.degrees(
math.atan2(local_vel[2], local_vel[0]))
fix.yawCalibration = math.degrees(
math.atan2(local_vel[1], local_vel[0]))
#fix.imuFrame = [(180/np.pi)*float(predicted_state[23]), (180/np.pi)*float(predicted_state[24]), (180/np.pi)*float(predicted_state[25])]
return fix
def liveLocationMsg(self, time):
fix = messaging.log.LiveLocationData.new_message()
predicted_state = self.kf.x
fix_ecef = predicted_state[0:3]
fix_pos_geo = coord.ecef2geodetic(fix_ecef)
fix.lat = float(fix_pos_geo[0])
fix.lon = float(fix_pos_geo[1])
fix.alt = float(fix_pos_geo[2])
fix.speed = float(np.linalg.norm(predicted_state[7:10]))
orientation_ned_euler = ned_euler_from_ecef(
fix_ecef, quat2euler(predicted_state[3:7]))
fix.roll = float(orientation_ned_euler[0] * 180 / np.pi)
fix.pitch = float(orientation_ned_euler[1] * 180 / np.pi)
fix.heading = float(orientation_ned_euler[2] * 180 / np.pi)
fix.gyro = [
float(predicted_state[10]),
float(predicted_state[11]),
float(predicted_state[12])
]
fix.accel = [
float(predicted_state[19]),
float(predicted_state[20]),
float(predicted_state[21])
]
local_vel = rotations_from_quats(predicted_state[3:7]).T.dot(
predicted_state[7:10])
fix.pitchCalibration = float(
(np.arctan2(local_vel[2], local_vel[0])) * 180 / np.pi)
fix.yawCalibration = float(
(np.arctan2(local_vel[1], local_vel[0])) * 180 / np.pi)
fix.imuFrame = [(180 / np.pi) * float(predicted_state[23]),
(180 / np.pi) * float(predicted_state[24]),
(180 / np.pi) * float(predicted_state[25])]
return fix
