Inertial Sensor Fusion
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.
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|
|Orientation estimation from a 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 |
|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
|Base class for defining sensor models used with
|AHRS||Orientation from accelerometer, gyroscope, and magnetometer readings|
- Choose Inertial Sensor Fusion Filters
Applicability and limitations of various inertial sensor fusion filters.
- Fuse Inertial Sensor Data Using insEKF-Based Flexible Fusion Framework
insEKFfilter object provides a flexible framework that you can use to fuse inertial sensor data.
- 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.