This repository implements the solution to Inertial Navigation System (INS) initialization. More precisely, the system estimates roll and pitch angles in addition to accelerometer and gyroscope biases (additionally yaw angle if magnetometer data is provided) so as to align the gravity vector direction along the local NED frame. The estimation occurs in two phases: coarse initialization followed by fine initialization. An Extended Kalman Filter (EKF) is used for fine initialization.

For mathematical treatment of the problem, head over to kvmanohar22.github.io/ins_init.

• Eigen 3.2+
• ROS melodic
• numpy (optional)
• matplotlib (optional)
• python3 (optional)

I have tested this on Arch Linux and should run out of the box on any Linux based distribution. Feel free to open an issue if faced with any problem.

• Build

  mkdir -p ins_ws/src
  cd ins_ws/src 
  git clone [email protected]:kvmanohar22/ins_init.git
  cd ..
  catkin_make

• Usage

Input to the system is stream of IMU (3 accelerometers, 3 gyroscopes) data. All the data used in this repository has been generated using pixhawk flight controller 2.4.8. Sample data is provided under assets/imu_*.bag.

  roslaunch ins_init test_ins_init_acc.launch

The above uses the provided bag under assets to estimate the state. To pass in your own bag,

  roslaunch ins_init test_ins_init_acc.launch bag_path:=/path/to/bag

OR

NOTE: The sensor has to be kept static during initialization.

if you have live stream of data. You might want to change ROS topic names in launch/test_ins_init_acc.launch or pass in on the command line as),

  roslaunch ins_init test_ins_init_acc.launch imu_topic:=<imu topic>

This was done using the data provided under assets directory which was collected using pixhawk 2.4.6 flight controller. The method estimates only roll and pitch angles with input being accelerometer data. Use of velocity observations will yield better results (to be implemented). Following plots show the evolution of covariance and the state (roll and pitch) itself.

• Acceleration observations
• Velocity observations (which estimates imu biases as well)
• Estimation of yaw
• Detailed Blog on estimation