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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ecompassOrientation from magnetometer and accelerometer readings
imufilterOrientation from accelerometer and gyroscope readings
ahrsfilterOrientation from accelerometer, gyroscope, and magnetometer readings
ahrs10filterHeight and orientation from MARG and altimeter readings
complementaryFilterOrientation estimation from a complementary filter
insfilterMARGEstimate pose from MARG and GPS data
insfilterAsyncEstimate pose from asynchronous MARG and GPS data
insfilterErrorStateEstimate pose from IMU, GPS, and monocular visual odometry (MVO) data
insfilterNonholonomicEstimate pose with nonholonomic constraints
insfilterCreate inertial navigation filter
tunerconfigFusion filter tuner configuration options
tunernoiseNoise structure of fusion filter

Blocks

AHRSOrientation from accelerometer, gyroscope, and magnetometer readings

Topics

Choose Inertial Sensor Fusion Filters

Applicability and Limitations of Inertial Sensor Fusion Filters.

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

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

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

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.

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Detect Noise in Sensor Readings with Residual Filtering

Detect Noise in Sensor Readings with Residual Filtering

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

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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IMU Sensor Fusion with Simulink

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.

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Sensor Fusion and Tracking Toolbox Documentation

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