Inertial Sensor Fusion

IMU and GPS sensor fusion to determine orientation and position

Use inertial sensor fusion algorithms to estimate orientation and position over time. The algorithms are optimized for different sensor configurations, output requirements, and motion constraints. You can directly fuse IMU data from multiple inertial sensors. You can also fuse IMU data with GPS data.

Functions

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Fuse IMU Data

`ecompass`	Orientation from magnetometer and accelerometer readings
`imufilter`	Orientation from accelerometer and gyroscope readings
`ahrsfilter`	Orientation from accelerometer, gyroscope, and magnetometer readings
`ahrs10filter`	Height and orientation from MARG and altimeter readings
`complementaryFilter`	Estimate orientation using complementary filter

Fuse IMU with GPS Data

`insfilterMARG`	Estimate pose from MARG and GPS data
`insfilterAsync`	Estimate pose from asynchronous MARG and GPS data
`insfilterErrorState`	Estimate pose from IMU, GPS, and monocular visual odometry (MVO) data
`insfilterNonholonomic`	Estimate pose with nonholonomic constraints
`insfilter`	Create inertial navigation filter

Flexible Inertial Sensor Fusion Filter

`insEKF`	Inertial Navigation Using Extended Kalman Filter (Since R2022a)
`insOptions`	Options for configuration of `insEKF` object (Since R2022a)
`insAccelerometer`	Model accelerometer readings for sensor fusion (Since R2022a)
`insGPS`	Model GPS readings for sensor fusion (Since R2022a)
`insGyroscope`	Model gyroscope readings for sensor fusion (Since R2022a)
`insMagnetometer`	Model magnetometer readings for sensor fusion (Since R2022a)
`insMotionOrientation`	Motion model for 3-D orientation estimation (Since R2022a)
`insMotionPose`	Model for 3-D motion estimation (Since R2022a)
`insCreateMotionModelTemplate`	Create template file for motion model (Since R2022b)
`insCreateSensorModelTemplate`	Create template file for sensor model (Since R2022b)
`positioning.INSMotionModel`	Base class for defining motion models used with `insEKF` (Since R2022a)
`positioning.INSSensorModel`	Base class for defining sensor models used with `insEKF` (Since R2022a)

Filter Tuning

`tunerconfig`	Fusion filter tuner configuration options (Since R2020b)
`tunernoise`	Noise structure of fusion filter (Since R2020b)
`tunerPlotPose`	Plot filter pose estimates during tuning (Since R2021a)

Blocks

AHRS	Orientation from accelerometer, gyroscope, and magnetometer readings (Since R2020a)
IMU Filter	Estimate orientation using IMU Filter (Since R2023b)
ecompass	Compute orientation from accelerometer and magnetometer readings (Since R2024a)
Complementary Filter	Estimate orientation using complementary filter (Since R2023a)

Topics

Choose Inertial Sensor Fusion Filters
Applicability and limitations of various inertial sensor fusion filters.
Fuse Inertial Sensor Data Using insEKF-Based Flexible Fusion Framework
The insEKF filter object provides a flexible framework that you can use to fuse inertial sensor data.
Determine Orientation Using Inertial Sensors
Fuse inertial measurement unit (IMU) readings to determine orientation.
Estimate Orientation Through Inertial Sensor Fusion
This example shows how to use 6-axis and 9-axis fusion algorithms to compute orientation.
Determine Pose Using Inertial Sensors and GPS
Use Kalman filters to fuse IMU and GPS readings to determine pose.
Logged Sensor Data Alignment for Orientation Estimation
This example shows how to align and preprocess logged sensor data.

Featured Examples

IMU and GPS Fusion for Inertial Navigation

How you might build an IMU + GPS fusion algorithm suitable for unmanned aerial vehicles (UAVs) or quadcopters.

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Estimate Orientation and Height Using IMU, Magnetometer, and Altimeter

Fuse data from a 3-axis accelerometer, 3-axis gyroscope, 3-axis magnetometer (together commonly referred to as a MARG sensor for Magnetic, Angular Rate, and Gravity), and 1-axis altimeter to estimate orientation and height.

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Pose Estimation from Asynchronous Sensors

How you might fuse sensors at different rates to estimate pose. Accelerometer, gyroscope, magnetometer and GPS are used to determine orientation and position of a vehicle moving along a circular path. You can use controls on the figure window to vary sensor rates and experiment with sensor dropout while seeing the effect on the estimated pose.

Open Script

Detect Multipath GPS Reading Errors Using Residual Filtering in Inertial Sensor Fusion

Use the residualgps object function and residual filtering to detect when new sensor measurements may not be consistent with the current filter state.

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Automatic Tuning of the insfilterAsync Filter

The insfilterAsync object is a complex extended Kalman filter that estimates the device pose. However, manually tuning the filter or finding the optimal values for the noise parameters can be a challenging task. This example illustrates how to use the tune function to optimize the filter noise parameters.

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Custom Tuning of Fusion Filters

Use the tune function to optimize the noise parameters of several fusion filters, including the ahrsfilter object. This example shows how to customize a cost function for various optimization goals.

Since R2021a
Open Live Script

IMU Sensor Fusion with Simulink

Generate and fuse IMU sensor data using Simulink®. You can accurately model the behavior of an accelerometer, a gyroscope, and a magnetometer and fuse their outputs to compute orientation.

Open Script

Design Fusion Filter for Custom Sensors

Introduces how to customize sensor models used with an insEKF object.

Since R2022a
Open Live Script

Estimate Orientation Using GPS-Derived Yaw Measurements

Define and use a custom sensor model for the insEKF object along with built-in sensor models. Using a custom yaw angle sensor, an accelerometer, and a gyroscope, this example uses the insEKF object to determine the orientation of a vehicle. You use the velocity from a GPS receiver to compute the yaw of the vehicle. Following a similar approach as shown in this example, you can develop custom sensor models for your own sensor fusion applications.

Since R2022a
Open Live Script

Ground Vehicle Pose Estimation for Tightly Coupled IMU and GNSS

Estimate the position and orientation of a ground vehicle by building a tightly coupled extended Kalman filter and using it to fuse sensor measurements. A tightly coupled filter fuses inertial measurement unit (IMU) readings with raw global navigation satellite system (GNSS) readings. In contrast, a loosely coupled filter fuses IMU readings with filtered GNSS receiver readings.

Since R2022a
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