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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