GNSS-IMU-SIM is an IMU simulation project, which generates reference trajectories, IMU sensor output, GPS output, odometer output and magnetometer output. Users choose/set up the sensor model, define the waypoints and provide algorithms, and gnss-imu-sim can generated required data for the algorithms, run the algorithms, plot simulation results, save simulations results, and generate a brief summary.
- Numpy ( version>1.10 )
- Matplotlib
We provide the following demos:
| file name | description |
|---|---|
| demo_no_algo.py | A demo of generating data, saving generated data to files and plotting (2D/3D)interested data, no user specified algorithm. |
| demo_allan.py | A demo of Allan analysis of gyroscope and accelerometer data. The generated Allan deviation is shown in figures. |
| demo_free_integration.py | A demo of a simple strapdown system. The simulation runs for 1000 times. The statistics of the INS results of the 1000 simulations are generated. |
| demo_inclinometer_mahony.py | A demo of an dynamic inclinometer algorithm based on Mahony's theory. This demos shows how to generate error plot of interested data. |
| demo_dmu380_sim.py | A demo of DMU380 algorithm. The DMU380 algorithm is first compiled as a shared library. This demo shows how to call the shared library. |
| demo_multiple_algorithms.py | A demo of multiple algorithms in a simulation. This demo shows how to compare resutls of multiple algorithm. |
| demo_gen_data_from_files.py | This demo shows how to generate data from logged data files. |
IMU error model can be specified in two ways:
There are three built-in IMU models: 'low-accuracy', 'mid-accuracy' and 'high accuracy'.
imu_err = {
# gyro bias, deg/hr
'gyro_b': np.array([0.0, 0.0, 0.0]),
# gyro angle random walk, deg/rt-hr
'gyro_arw': np.array([0.25, 0.25, 0.25]),
# gyro bias instability, deg/hr
'gyro_b_stability': np.array([3.5, 3.5, 3.5]),
# gyro bias instability correlation, sec.
# set this to 'inf' to use a random walk model
# set this to a positive real number to use a first-order Gauss-Markkov model
'gyro_b_corr': np.array([100.0, 100.0, 100.0]),
# accelerometer bias, m/s^2
'accel_b': np.array([0.0e-3, 0.0e-3, 0.0e-3]),
# accelerometer velocity random walk, m/s/rt-hr
'accel_vrw': np.array([0.03119, 0.03009, 0.04779]),
# accelerometer bias instability, m/s^2
'accel_b_stability': np.array([4.29e-5, 5.72e-5, 8.02e-5]),
# accelerometer bias instability correlation, sec. Similar to gyro_b_corr
'accel_b_corr': np.array([200.0, 200.0, 200.0]),
# magnetometer noise std, uT
'mag_std': np.array([0.2, 0.2, 0.2])
}imu = imu_model.IMU(accuracy=imu_err, axis=6, gps=False)
imu = imu_model.IMU(accuracy='low accuracy', axis=9, gps=True)axis = 6 to generate only gyro and accelerometer data.
axis = 9 to generate magnetometer data besides gyro and accelerometer data.
gps = True to generate GPS data, gps = False not.
algo = allan_analysis.Allan() # an Allan analysis demo algorithmAn algorithm is an object of a Python class. It should at least include the following members:
The member variable 'input' tells gnss-imu-sim what data the algorithm need. 'input' is a tuple or list of strings. Each string in 'input' corresponds to a set of data generated and provided by gnss-imu-sim.
Supported input:
| name | description |
|---|---|
| 'ref_frame' | Reference frame. 0 to use the NED frame as the navigation frame, 1 to use a virtual inertial frame. For the NED frame, its origin is located at the vehicle center, its x axis is in the local horizontal plane and points northwards, its y axis is in the local horizontal plane and points eastwards, and its z axis points downwards. For the virtual inertial frame, the Earth rotation will be ignored. This frame can be considered as an NED frame fixed at the initial position. |
| 'time' | Time series corresponds to IMU samples, units: sec. |
| 'fs' | Sample frequency of IMU, units: Hz |
| 'ref_pos' | True position in the navigation frame. When users choose NED (ref_frame=0) as the navigation frame, positions will be given in the form of [Latitude, Longitude, Altitude], units: ['rad', 'rad', 'm']. When users choose the virtual inertial frame, positions (initial position + positions relative to the origin of the frame) will be given in the form of [x, y, z], units: ['m', 'm', 'm']. |
| 'ref_vel' | True velocity w.r.t the navigation/reference frame expressed in the body frame, units: ['m/s', 'm/s', 'm/s']. |
| 'ref_att_euler' | True attitude (Euler angles, ZYX rotation sequency), units: ['rad', 'rad', 'rad'] |
| 'ref_att_quat' | True attitude (quaternions) |
| 'ref_gyro' | True angular velocity in the body frame, units: ['rad/s', 'rad/s', 'rad/s'] |
| 'ref_accel' | True acceleration in the body frame, units: ['m/s^2', 'm/s^2', 'm/s^2'] |
| 'ref_mag' | True geomagnetic field in the body frame, units: ['uT', 'uT', 'uT'] (only available when axis=9 in IMU object) |
| 'ref_gps' | True GPS position/velocity, ['rad', 'rad', 'm', 'm/s', 'm/s', 'm/s'] for NED (LLA), ['m', 'm', 'm', 'm/s', 'm/s', 'm/s'] for virtual inertial frame (xyz) (only available when gps=True in IMU object) |
| 'gps_time' | Time series correspond to GPS samples, units: sec |
| 'gyro' | Gyroscope measurements, 'ref_gyro' with errors |
| 'accel' | Accelerometer measurements, 'ref_accel' with errors |
| 'mag' | Magnetometer measurements, 'ref_mag' with errors |
| 'gps' | GPS measurements, 'ref_gps' with errors |
The member variable 'output' tells gnss-imu-sim what data the algorithm returns. 'output' is a tuple or list of strings. Each element in 'output' corresponds to a set of data that can be understood by gnss-imu-sim.
Supported output:
| name | description |
|---|---|
| 'algo_time' | Time series corresponding to algorithm output, units: ['s'] |
| 'allan_t' | Time series of Allan analysis, units: ['s'] |
| 'allan_std_gyro' | Allan std of gyro, units: ['rad/s', 'rad/s', 'rad/s'] |
| 'allan_std_accel' | Allan std of accel, units: ['m/s2', 'm/s2', 'm/s2'] |
| 'pos' | Simulation position from algo, units: ['rad', 'rad', 'm'] for NED (LLA), ['m', 'm', 'm'] for virtual inertial frame (xyz). |
| 'vel' | Simulation velocity from algo, units: ['m/s', 'm/s', 'm/s'] |
| 'att_euler' | Simulation attitude (Euler, ZYX) from algo, units: ['rad', 'rad', 'rad'] |
| 'att_quat' | Simulation attitude (quaternion) from algo |
| 'wb' | Gyroscope bias estimation, units: ['rad/s', 'rad/s', 'rad/s'] |
| 'ab' | Accelerometer bias estimation, units: ['m/s^2', 'm/s^2', 'm/s^2'] |
| value | description |
|---|---|
| batch=True | Put all data from t0 to tf (default) |
| batch=False | Sequentially put data from t0 to tf (not supported yet) |
This is the main procedure of the algorithm. gnss-imu-sim will call this procedure to run the algorithm. 'set_of_input' is a list of data that is consistent with self.input. For example, if you set self.input = ['fs', 'accel', 'gyro'], you should get the corresponding data this way:
def run(self, set_of_input):
# get input
fs = set_of_input[0]
accel = set_of_input[1]
gyro = set_of_input[2]gnss-imu-sim will call this procedure to get resutls from the algorithm. The return should be consistent with self.output. For example, if you set self.output = ['allan_t', 'allan_std_accel', 'allan_std_gyro'], you should return the results this way:
def get_results(self):
self.results = [tau,
np.array([avar_ax, avar_ay, avar_az]).T,
np.array([avar_wx, avar_wy, avar_wz]).T]
return self.resultsgnss-imu-sim will call this procedure after run the algorithm. This is necessary when you want to run the algorithm more than one time and some states of the algorithm should be reinitialized.
sim = ins_sim.Sim(
# sample rate of imu (gyro and accel), GPS and magnetometer
[fs, fs_gps, fs_mag],
# initial conditions and motion definition,
# see IMU in imu_sim.py for details
data_path+"//motion_def-90deg_turn.csv",
# reference frame
ref_frame=1,
# the imu object created at step 1
imu,
# vehicle maneuver capability
# [max accel, max angular accel, max angular rate]
mode=np.array([1.0, 0.5, 2.0]),
# specifies the vibration model for IMU
env=None,
#env=np.genfromtxt(data_path+'//vib_psd.csv', delimiter=',', skip_header=1),
# the algorithm object created at step 2
algorithm=algo)ins_sim.Sim supports running multiple algorithms in one simulation. All the algorithms should have the same input and same output. In this case, the parameter algorithm should be a list of user defined algorithms.
There are three kinds of vibration models:
| vibration model | description |
|---|---|
| 'ng-random' | normal-distribution random vibration, rms is n*9.8 m/s^2 |
| 'n-random' | normal-distribution random vibration, rms is n m/s^2 |
| 'ng-mHz-sinusoidal' | sinusoidal vibration of m Hz, amplitude is n*9.8 m/s^2 |
| 'n-mHz-sinusoidal' | sinusoidal vibration of m Hz, amplitude is n m/s^2 |
| numpy array of size (n,4) | single-sided PSD. [freqency, x, y, z], m^2/s^4/Hz |
sim.run() # run for 1 time
sim.run(1) # run for 1 time
sim.run(100) # run for 100 times# generate a simulation summary,
# and save the summary and all data in directory './data'.
# You can specify the directory.
sim.results('./data/')
# generate a simulation summary, do not save any file
sim.results()
# plot interested data
sim.plot(['ref_pos', 'gyro'], opt={'ref_pos': '3d'})- Geomagnetic field model https://github.com/cmweiss/geomag/tree/master/geomag
- MRepo http://www.instk.org