plot_imu_err_with_cov {navigation}

Plot IMU error with covariances

Description

this function plots the estimated IMU errors with covariance of a solution computed with the navigation function

Usage

plot_imu_err_with_cov(sol, idx = 1, error = TRUE, step = 10)

Arguments

sol

The set of solutions returned by the navigation function

idx

Which Monte-Carlo solution to plot

error

Whether to plot the error with respect to the reference or the estimated values

step

Plot one time out of step

Value

A plot of the estimated IMU errors with covariance.

Author(s)

Davide Cucci, Lionel Voirol, Mehran Khaghani, Stéphane Guerrier

Examples

data("lemniscate_traj_ned")
head(lemniscate_traj_ned)
traj <- make_trajectory(data = lemniscate_traj_ned,
 system = "ned")
plot(traj)
timing <- make_timing(
  nav.start = 0, # time at which to begin filtering
  nav.end = 20,
  freq.imu = 100, 
  # frequency of the IMU, can be slower wrt trajectory frequency
  freq.gps = 1, 
  # GNSS frequency
  freq.baro = 1, 
  # barometer frequency (to disable, put it very low, e.g. 1e-5)
  gps.out.start = 10, # to simulate a GNSS outage, set a time before nav.end
  gps.out.end =15
)
# create sensor for noise data generation
snsr.mdl <- list()
# this uses a model for noise data generation
acc.mdl <- WN(sigma2 = 5.989778e-05) +
  AR1(phi = 9.982454e-01, sigma2 = 1.848297e-10) +
  AR1(phi = 9.999121e-01, sigma2 = 2.435414e-11) +
  AR1(phi = 9.999998e-01, sigma2 = 1.026718e-12)
gyr.mdl <- WN(sigma2 = 1.503793e-06) +
  AR1(phi = 9.968999e-01, sigma2 = 2.428980e-11) +
  AR1(phi = 9.999001e-01, sigma2 = 1.238142e-12)
snsr.mdl$imu <- make_sensor(
  name = "imu",
  frequency = timing$freq.imu,
  error_model1 = acc.mdl,
  error_model2 = gyr.mdl
)
# RTK-like GNSS
gps.mdl.pos.hor <- WN(sigma2 = 0.025^2)
gps.mdl.pos.ver <- WN(sigma2 = 0.05^2)
gps.mdl.vel.hor <- WN(sigma2 = 0.01^2)
gps.mdl.vel.ver <- WN(sigma2 = 0.02^2)
snsr.mdl$gps <- make_sensor(
  name = "gps",
  frequency = timing$freq.gps,
  error_model1 = gps.mdl.pos.hor,
  error_model2 = gps.mdl.pos.ver,
  error_model3 = gps.mdl.vel.hor,
  error_model4 = gps.mdl.vel.ver
)
# Barometer
baro.mdl <- WN(sigma2 = 0.5^2)
snsr.mdl$baro <- make_sensor(
  name = "baro",
  frequency = timing$freq.baro,
  error_model1 = baro.mdl
)
# define sensor for Kalmna filter
KF.mdl <- list()
# make IMU sensor
KF.mdl$imu <- make_sensor(
  name = "imu",
  frequency = timing$freq.imu,
  error_model1 = acc.mdl,
  error_model2 = gyr.mdl
)
KF.mdl$gps <- snsr.mdl$gps
KF.mdl$baro <- snsr.mdl$baro
# perform navigation simulation
num.runs <- 5 # number of Monte-Carlo simulations
res <- navigation(
  traj.ref = traj,
  timing = timing,
  snsr.mdl = snsr.mdl,
  KF.mdl = KF.mdl,
  num.runs = num.runs,
  noProgressBar = TRUE,
  PhiQ_method = "2",
  # order of the Taylor expansion of the matrix exponential used to compute Phi and Q matrices
  compute_PhiQ_each_n = 10,
  # compute new Phi and Q matrices every n IMU steps (execution time optimization)
  parallel.ncores = 1,
  P_subsampling = timing$freq.imu
)
plot_imu_err_with_cov(res, error=FALSE)