Inertial Sensor Fusion

Inertial navigation with IMU and GPS, sensor fusion, custom filter tuning

Inertial sensor fusion uses filters to improve and combine readings from IMU, GPS, and other sensors. To model specific sensors, see Sensor Models.

For simultaneous localization and mapping, see SLAM.

Functions

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Fuse IMU Data

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

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
`insOptions`	Options for configuration of `insEKF` object
`insAccelerometer`	Model accelerometer readings for sensor fusion
`insGPS`	Model GPS readings for sensor fusion
`insGyroscope`	Model gyroscope readings for sensor fusion
`insMagnetometer`	Model magnetometer readings for sensor fusion
`insMotionOrientation`	Motion model for 3-D orientation estimation
`insMotionPose`	Model for 3-D motion estimation
`insCreateMotionModelTemplate`	Create template file for motion model
`insCreateSensorModelTemplate`	Create template file for sensor model
`positioning.insMotionModel`	Base class for defining motion models used with `insEKF`
`positioning.insSensorModel`	Base class for defining sensor models used with `insEKF`

Filter Tuning

`tunerconfig`	Fusion filter tuner configuration options
`tunerPlotPose`	Plot filter pose estimates during tuning

Blocks

AHRS	Orientation from accelerometer, gyroscope, and magnetometer readings
Complementary Filter	Estimate orientation using complementary filter

Topics

Sensor Fusion

Choose Inertial Sensor Fusion Filters
Applicability and limitations of various inertial sensor fusion filters.
Estimate Orientation Through Inertial Sensor Fusion
This example shows how to use 6-axis and 9-axis fusion algorithms to compute orientation.
Estimate Orientation with a Complementary Filter and IMU Data
This example shows how to stream IMU data from an Arduino and estimate orientation using a complementary filter.
Logged Sensor Data Alignment for Orientation Estimation
This example shows how to align and preprocess logged sensor data.
Lowpass Filter Orientation Using Quaternion SLERP
This example shows how to use spherical linear interpolation (SLERP) to create sequences of quaternions and lowpass filter noisy trajectories.
Pose Estimation From Asynchronous Sensors
This example shows how you might fuse sensors at different rates to estimate pose.
Custom Tuning of Fusion Filters
Use the tune function to optimize the noise parameters of several fusion filters, including the ahrsfilter object.
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.
Autonomous Underwater Vehicle Pose Estimation Using Inertial Sensors and Doppler Velocity Log
This example shows how to fuse data from a GPS, Doppler Velocity Log (DVL), and inertial measurement unit (IMU) sensors to estimate the pose of an autonomous underwater vehicle (AUV) shown in this image.

Applications

Binaural Audio Rendering Using Head Tracking
Track head orientation by fusing data received from an IMU, and then control the direction of arrival of a sound source by applying head-related transfer functions (HRTF).
Estimating Orientation Using Inertial Sensor Fusion and MPU-9250
This example shows how to get data from an InvenSense MPU-9250 IMU sensor, and to use the 6-axis and 9-axis fusion algorithms in the sensor data to compute orientation of the device.
Wireless Data Streaming and Sensor Fusion Using BNO055
This example shows how to get data from a Bosch BNO055 IMU sensor through an HC-05 Bluetooth® module, and to use the 9-axis AHRS fusion algorithm on the sensor data to compute orientation of the device.

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 Position and Orientation of a Ground Vehicle

Estimate the position and orientation of ground vehicles by fusing data from an inertial measurement unit (IMU) and a global positioning system (GPS) receiver.

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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.

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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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Estimate Phone Orientation Using Sensor Fusion

MATLAB Mobile™ reports sensor data from the accelerometer, gyroscope, and magnetometer on Apple or Android mobile devices. Raw data from each sensor or fused orientation data can be obtained. This examples shows how to compare the fused orientation data from the phone with the orientation estimate from the ahrsfilter object.

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Visual-Inertial Odometry Using Synthetic Data

Estimate the pose (position and orientation) of a ground vehicle using an inertial measurement unit (IMU) and a monocular camera. In this example, you:

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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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Design Fusion Filter for Custom Sensors

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

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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.

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Estimate Orientation Using AHRS Filter and IMU Data in Simulink

Stream IMU data from sensors connected to Arduino® board and estimate orientation using AHRS filter and IMU sensor.

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