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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
insEKFInertial Navigation Using Extended Kalman Filter
insOptionsOptions for configuration of insEKF object
insAccelerometerModel accelerometer readings for sensor fusion
insGPSModel GPS readings for sensor fusion
insGyroscopeModel gyroscope readings for sensor fusion
insMagnetometerModel magnetometer readings for sensor fusion
insMotionOrientationMotion model for 3-D orientation estimation
insMotionPoseModel for 3-D motion estimation
insCreateMotionModelTemplateCreate template file for motion model
insCreateSensorModelTemplateCreate template file for sensor model
positioning.INSMotionModelBase class for defining motion models used with insEKF
positioning.INSSensorModelBase class for defining sensor models used with insEKF
tunerconfigFusion filter tuner configuration options
tunernoiseNoise structure of fusion filter
tunerPlotPosePlot filter pose estimates during tuning

Blocks

AHRSOrientation from accelerometer, gyroscope, and magnetometer readings

Topics