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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

fuse imu data

	orientation from magnetometer and accelerometer readings
	orientation from accelerometer and gyroscope readings
	orientation from accelerometer, gyroscope, and magnetometer readings
	height and orientation from marg and altimeter readings
	estimate orientation using complementary filter

fuse imu with gps data

	estimate pose from marg and gps data
	estimate pose from asynchronous marg and gps data
	estimate pose from imu, gps, and monocular visual odometry (mvo) data
	estimate pose with nonholonomic constraints
	create inertial navigation filter

flexible inertial sensor fusion filter

	inertial navigation using extended kalman filter
	options for configuration of `insekf` object
	model accelerometer readings for sensor fusion
	model gps readings for sensor fusion
	model gyroscope readings for sensor fusion
	model magnetometer readings for sensor fusion
	motion model for 3-d orientation estimation
	model for 3-d motion estimation
	create template file for motion model
	create template file for sensor model
	base class for defining motion models used with `insekf`
	base class for defining sensor models used with `insekf`

filter tuning

	fusion filter tuner configuration options
	noise structure of fusion filter
	plot filter pose estimates during tuning

blocks

	orientation from accelerometer, gyroscope, and magnetometer readings
	estimate orientation using complementary filter

topics

choose inertial sensor fusion filters
applicability and limitations of various inertial sensor fusion filters.
the insekf filter object provides a flexible framework that you can use to fuse inertial sensor data.
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.
use kalman filters to fuse imu and gps readings to determine pose.
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.

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.

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.

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.