HOW TO EVALUATE THE PERFORMANCE OF AN INERTIAL MEASUREMENT UNIT (IMU)

Are you looking at the many different IMUs available and scratching your head, baffled by the different specifications quoted against each?

Price, weight, and performance are the three important factors to consider and the importance of each will depend on what you want to do with it - confusing right?!

We know A LOT about IMUs - we have been working with and testing them for years and we fully understand how complex they can be. Here, we aim to help you understand the ins and outs of the technical jargon so you can feel more confident in selecting the right IMU for you.

How to assess the performance of an inertial navigation system (INS)

The performance of an IMU is typically presented as various types of IMU error. The most commonly quoted errors are briefly summarised below.

INS Performance Units Description
Position m The absolute positional accuracy
Velocity m/s The velocity accuracy
Roll/Pitch deg The roll/pitch accuracy
True Heading deg The heading accuracy
Data Rates Hz The measurement speed of the IMU

The performance of the INS (Inertial Navigation System) is also often quoted and takes into account the accuracy of the GNSS and IMU when combined by a Kalman Filter (KF)

An INS filter, referred to as a Kalman Filter (KF), combines the different measurements taking into account estimated errors to produce a trajectory including time, position, and attitude.

IMU Error Units Description
Bias Repeatability deg/hr or m/s2 The difference between the real value and the output, which can change from mission to mission and affects the time taken to initialise the INS
Bias Stability deg/hr2 The bias can change over time and has an effect on the performance of the INS during GNSS outage
Scale Factor ppm The releation in scale between the input and output
Random walk deg/√hr The noise of the sensor, which causes INS error to grow over time during GNSS outage

For long range applications, the important features to consider will be the accuracy of the heading, roll and pitch, since these errors grow with range. A high data rate is also important because scanners will generally be recording at a much higher rate than the IMU and so errors with interpolation will occur which will also grow with range.

If the application requires driving with very little dynamics (low speed, very few turns) then an IMU with a low bias and low random walk will be beneficial. It should be noted that heading drift can be reduced during a low dynamic survey by using a dual GNSS antenna system.

For surveying in areas of poor GNSS, such as city mapping, the most important factor will be the bias, and random walk since these errors dictate how quickly and to what extent heading drift occurs. For example, if the INS has no GNSS for one minute, a low grade IMU could result in an error of over 2m, and a high grade IMU an error of less than 20cm.

A further factor to consider is how the INS and GNSS interact with each other, a term referred to as coupling. A loosely coupled INS is where the position and velocity from the INS and GNSS are combined to form a trajectory. A tightly coupled INS goes a step further by also combining the GNSS raw measurements with the INS, therefore allowing GNSS position updates with fewer than four satellites (four satellites are usually the minimum requirement for a GNSS position fix). The deeply coupled approach is similar to the tightly coupled approach, except information is also passed from the INS filter to the GNSS filter, which enabled faster GNSS signal reacquisition.

It is common for some INS’ to use a combination of these different coupling techniques and will generally be given different terminology to those described above, so it may be necessary to ask the supplier how the INS and GNSS interact with each other.

Typical INS performance descriptions

The performance of the INS (Inertial Navigation System) is also often quoted and takes into account the accuracy of the GNSS and IMU when combined by a Kalman Filter (KF)

An INS filter, referred to as a Kalman Filter (KF), combines the different measurements taking into account estimated errors to produce a trajectory including time, position, and attitude.

Typical IMU error descriptions

For long range applications, the important features to consider will be the accuracy of the heading, roll and pitch, since these errors grow with range. A high data rate is also important because scanners will generally be recording at a much higher rate than the IMU and so errors with interpolation will occur which will also grow with range.

If the application requires driving with very little dynamics (low speed, very few turns) then an IMU with a low bias and low random walk will be beneficial. It should be noted that heading drift can be reduced during a low dynamic survey by using a dual GNSS antenna system.

For surveying in areas of poor GNSS, such as city mapping, the most important factor will be the bias, and random walk since these errors dictate how quickly and to what extent heading drift occurs. For example, if the INS has no GNSS for one minute, a low grade IMU could result in an error of over 2m, and a high grade IMU an error of less than 20cm.

A further factor to consider is how the INS and GNSS interact with each other, a term referred to as coupling. A loosely coupled INS is where the position and velocity from the INS and GNSS are combined to form a trajectory. A tightly coupled INS goes a step further by also combining the GNSS raw measurements with the INS, therefore allowing GNSS position updates with fewer than four satellites (four satellites are usually the minimum requirement for a GNSS position fix). The deeply coupled approach is similar to the tightly coupled approach, except information is also passed from the INS filter to the GNSS filter, which enabled faster GNSS signal reacquisition.

It is common for some INS’ to use a combination of these different coupling techniques and will generally be given different terminology to those described above, so it may be necessary to ask the supplier how the INS and GNSS interact with each other.

Typical INS performance descriptions

The performance of the INS (Inertial Navigation System) is also often quoted and takes into account the accuracy of the GNSS and IMU when combined by a Kalman Filter (KF)

An INS filter, referred to as a Kalman Filter (KF), combines the different measurements taking into account estimated errors to produce a trajectory including time, position, and attitude.

Typical IMU error descriptions

For long range applications, the important features to consider will be the accuracy of the heading, roll and pitch, since these errors grow with range. A high data rate is also important because scanners will generally be recording at a much higher rate than the IMU and so errors with interpolation will occur which will also grow with range.

If the application requires driving with very little dynamics (low speed, very few turns) then an IMU with a low bias and low random walk will be beneficial. It should be noted that heading drift can be reduced during a low dynamic survey by using a dual GNSS antenna system.

For surveying in areas of poor GNSS, such as city mapping, the most important factor will be the bias, and random walk since these errors dictate how quickly and to what extent heading drift occurs. For example, if the INS has no GNSS for one minute, a low grade IMU could result in an error of over 2m, and a high grade IMU an error of less than 20cm.

A further factor to consider is how the INS and GNSS interact with each other, a term referred to as coupling. A loosely coupled INS is where the position and velocity from the INS and GNSS are combined to form a trajectory. A tightly coupled INS goes a step further by also combining the GNSS raw measurements with the INS, therefore allowing GNSS position updates with fewer than four satellites (four satellites are usually the minimum requirement for a GNSS position fix). The deeply coupled approach is similar to the tightly coupled approach, except information is also passed from the INS filter to the GNSS filter, which enabled faster GNSS signal reacquisition.

It is common for some INS’ to use a combination of these different coupling techniques and will generally be given different terminology to those described above, so it may be necessary to ask the supplier how the INS and GNSS interact with each other.

Want to know more?

If you have any questions about IMUs, INSs or anything else please get in touch.

Our technical team would love to talk to you.