GNSS-INS-SIM

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GNSS-INS-SIM is an GNSS/INS 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-ins-sim can generate required data for the algorithms, run the algorithms, plot simulation results, save simulations results, and generate a brief summary.

Contents

• Requirements
• Demos
• Get started
  • Step 1 Define the IMU model
  • Step 2 Create a motion profile
  • Step 3 Create your algorithm
  • Step 4 Run the simulation
  • Step 5 Show results
• Acknowledgement

Requirements

• Numpy ( version>1.10 )
• Matplotlib

Demos

We provide the following demos to show how to use this tool:

Get started

Step 1 Define the IMU model

Step 1.1 Define the IMU error model

IMU error model can be specified in two ways:

Choose a built-in model

There are three built-in IMU models: ‘low-accuracy’, ‘mid-accuracy’ and ‘high accuracy’.

Manually define the model

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])
          }

Step 1.2 Create an IMU object

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.

Step 2 Create a motion profile

A motion profile specifies the initial states of the vehicle and motion command that drives the vehicle to move, as shown in the following table.

File truncated at 100 lines see the full file