The Difference Between IMU, AHRS, and INS

Shopping around for an inertial sensor, people think of INS. Maybe you need an IMU, which is super simple. Understanding the difference between IMU, AHRS, and INS is going to help pick the right product for your specific application. Watch the video below as Morgan explains the difference between IMU and AHRS, as well as a complete breakdown of INS.
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Timestamps:
1:49 IMU Inertial Measurement Unit - Raw Data without Navigation
3:11 AHRS - Attitude Heading Reference System - An IMU + GPS Positioning
3:44 INS - Inertial Navigation System - Smart Navigation
4:38 RTK Real-Time Kinematics - Centimeter-level Accuracy for Your INS
7:42 RTK + Compassing - Why is Compassing Paired With RTK?
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Additional Autonomous Navigation Resources
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An IMU only consists of a magnetometer, accelerometer, and gyro. Sometimes magnetometer is not included in the package. It will not have a smart-system.
AHRS stands for attitude heading reference system and includes a GPS, but no Kalman filter. It has all the benefits of the IMU, plus GPS position. This is a good fit if there is already a filter in mind to be designed, or already incorporated.
INS is an inertial navigation system. It takes all the sensors, fusing them into one system. It knows exactly where it is in the world based on just that output. INS is not a GPS. It has a Kalman filter in it, which is how the sensor itself fuses all the individual parts into one and gives navigation output with everything incorporated. It has a sensor fusion built into the device, giving a more accurate output, and includes a gyro, magnetometer, accelerometer, and GPS. This allows your robot to understand where it is in the world.
The Kalman filter is a software package. The Kalman filter gives a fused output that the rest of your robot can run on. RTK, real-time kinematics, is a process where GPS position is taken from a base station, and output corrections are sent to your rover. The value of using an RTK base is if you work off any type of triangulation base, you can get a position. With the two GPS system combination, you can get centimeter-level accuracy. This is only available in the INS system. Another feature only accessible with INS is compassing. Based on the distance between the two GPS positions, you can derive heading, giving accurate heading dimensions to your regular navigation system.
The be-all solution that everyone really wants is, can I get great compassing heading and have position accuracy too with RTK? The answer is yes!
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[@fredmapes8414]

Listening to up-dated definitions is interesting. I worked the F-4 aircraft, and our INS (APN-46 and APN-48) was just a stable platform with gyros and accelerometers. The F-4D used LORAN to do bombing runs. I also worked DOPPLER on cargo aircraft.

[@a1droneshots]

Great work, very clear and helpful explanations. Thank you!

[@motonny482]

very clear ,thanks man

[@Min-eo1dq]

Thanks a lot this is exactly what I need.

[@mohamedalhashmi6322]

Well explained.

[@amortalbeing]

Thanks a lot really appreciate it

[@steffanieabadier9439]

ohh so this is where Luka Doncic works during the off season

[@timd9430]

Can a simple 9DOF IMU device (no RTK etc) know it's own ongoing XYZ location if just a 0,0,0 start origin is provided? I.e. earth, start at any XYZ location on the surface of the earth. Traverse to multiple points of interest, then return exactly back to origin? And can all of this still be within CM accuracy depending on IMU? Again, not with the help of RTK, no GNSS, no optics, etc. Just 9DOF data and it's starting origin and some sort of leveling or direction of control for X and Y. Thanks! Great explanation!!!

[@davidrediger6407]

A simple IMU can only measure the angular or linear motion that acts upon it. If you keep track all to the movements that have acted on it you still can not retrace its steps to get back to its origin because the earth has rotated underneath it. A vehicle travels over the earths surface perpendicular to the center of the earth. A gyro stays stationary in space which will make the gyro appear to flip at a 24 hour rate. Unless you torque the IMU platform as a function of latitude to keep it perpendicular to the center of the earth or calculate the earths rotation under you, just a bare bones IMU will get you lost every time.
I am an Old US Navy Submarine Sailor (1973-1985) Electronics Technician that operated and maintained a Ships Inertial Navigation System. In my day the IMU weighted about a tone and was controlled by a 3rd generation discrete component computer with only a 1k magnetic core memory and a 1k magnetic drum memory. We did use a 13-state Kalman filter to calculate resets for the torqueing values being sent to the gyros to keep the binnacle platform stable so the accelerometers can measure accurate velocities so it can keep trake of our position using a dead reckoning calculation.

[@EriccoInertialsystem]

@@davidrediger6407 You are too professional. I don’t understand the working principle of MEMS sensors applied to aircraft. Can we communicate in depth?

[@gsxr600rm]

iNS sounds very much like an ADC(air data computer). Coming from a small GA sing engine pilot perspective. Feel free to enlighten me

[@MagnusWendt]

RTK is in no way a feature "exclusive to INS". They are not even related. You can have an RTK-GNSS with or without compassing without any kind of INS.

[@larafelix2197]

sounds like his trying to sell me an ins

[@Hopeless_and_Forlorn]

If this is supposed to be a comical misinterpretation of inertial navigation, that has already been done. If you are even remotely serious about what you are saying, you are doing a disservice to your viewers. You may have read an article that emphasizes the role of Kalman filters in navigation systems, but I guarantee you that you do not know the difference between a flux valve and a Euler's pendulum.

[@davidrediger6407]

A Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, who was one of the primary developers of its theory.
Using a Kalman filter you can predict with a high degree of accuracy which horse will win a race. The more variables you add to the equation the more accurate it will become.

[@AhmedDeedatPalestine]

Its not rocket science, the way he is selling to dumb people is frustrating

[@mrobbetti]

All wrong