慣性センサーフュージョン

IMU と GPS による慣性ナビゲーション、センサーフュージョン、カスタムフィルター調整

慣性センサーフュージョンは、フィルターを使用して IMU や GPS などのセンサーの読み取り値を改善し、組み合わせます。特定のセンサーをモデル化する場合は、センサーモデルを参照してください。

自己位置推定と環境地図作成の同時実行については、SLAMを参照してください。

関数

IMU データの融合

`ahrsfilter`	Orientation from accelerometer, gyroscope, and magnetometer readings
`ahrs10filter`	Height and orientation from MARG and altimeter readings
`complementaryFilter`	Estimate orientation using complementary filter
`ecompass`	Orientation from magnetometer and accelerometer readings
`imufilter`	Orientation from accelerometer and gyroscope readings

IMU と GPS データの融合

`insfilterMARG`	Estimate pose from MARG and GPS data
`insfilterAsync`	Estimate pose from asynchronous MARG and GPS data
`insfilterErrorState`	Estimate pose from IMU, GPS, and monocular visual odometry (MVO) data
`insfilterNonholonomic`	Estimate pose with nonholonomic constraints
`insfilter`	慣性ナビゲーションフィルターを作成

柔軟な慣性センサーフュージョンフィルター

`insEKF`	Inertial Navigation Using Extended Kalman Filter
`insOptions`	Options for configuration of `insEKF` object
`insAccelerometer`	Model accelerometer readings for sensor fusion
`insGPS`	Model GPS readings for sensor fusion
`insGyroscope`	Model gyroscope readings for sensor fusion
`insMagnetometer`	Model magnetometer readings for sensor fusion
`insMotionOrientation`	Motion model for 3-D orientation estimation
`insMotionPose`	Model for 3-D motion estimation
`insCreateMotionModelTemplate`	Create template file for motion model
`insCreateSensorModelTemplate`	Create template file for sensor model
`positioning.insMotionModel`	Base class for defining motion models used with `insEKF`
`positioning.insSensorModel`	Base class for defining sensor models used with `insEKF`

フィルター調整

`tunerconfig`	Fusion filter tuner configuration options
`tunerPlotPose`	Plot filter pose estimates during tuning

ブロック

AHRS	Orientation from accelerometer, gyroscope, and magnetometer readings

トピック

センサーフュージョン

Choose Inertial Sensor Fusion Filters
Applicability and limitations of various inertial sensor fusion filters.
Estimate Orientation Through Inertial Sensor Fusion
This example shows how to use 6-axis and 9-axis fusion algorithms to compute orientation. There are several algorithms to compute orientation from inertial measurement units (IMUs) and magnetic-angular rate-gravity (MARG) units. This example covers the basics of orientation and how to use these algorithms.
Estimate Orientation with a Complementary Filter and IMU Data
This example shows how to stream IMU data from an Arduino and estimate orientation using a complementary filter.
Logged Sensor Data Alignment for Orientation Estimation
This example shows how to align and preprocess logged sensor data. This allows the fusion filters to perform orientation estimation as expected. The logged data was collected from an accelerometer and a gyroscope mounted on a ground vehicle.
Lowpass Filter Orientation Using Quaternion SLERP
This example shows how to use spherical linear interpolation (SLERP) to create sequences of quaternions and lowpass filter noisy trajectories. SLERP is a commonly used computer graphics technique for creating animations of a rotating object.
Pose Estimation From Asynchronous Sensors
This example shows 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.
Custom Tuning of Fusion Filters
Use the tune function to optimize the noise parameters of several fusion filters, including the ahrsfilter object. This example shows how to custom a cost function for various optimization goals.
Fuse Inertial Sensor Data Using insEKF-Based Flexible Fusion Framework
The insEKF filter object provides a flexible framework that you can use to fuse inertial sensor data. You can fuse measurement data from various inertial sensors by selecting or customizing the sensor models used in the filter, and estimate different platform states by selecting or customizing the motion model used in the filter. The insEKF (Sensor Fusion and Tracking Toolbox)insEKF object is based on a continuous-discrete extended Kalman filter, in which the state prediction step is continuous, and the measurement correction or fusion step is discrete.

アプリケーション

Binaural Audio Rendering Using Head Tracking
Track head orientation by fusing data received from an IMU, and then control the direction of arrival of a sound source by applying head-related transfer functions (HRTF).
Estimating Orientation Using Inertial Sensor Fusion and MPU-9250
This example shows how to get data from an InvenSense MPU-9250 IMU sensor, and to use the 6-axis and 9-axis fusion algorithms in the sensor data to compute orientation of the device.
Wireless Data Streaming and Sensor Fusion Using BNO055
This example shows how to get data from a Bosch BNO055 IMU sensor through an HC-05 Bluetooth® module, and to use the 9-axis AHRS fusion algorithm on the sensor data to compute orientation of the device. The example creates a figure which gets updated as you move the device.

注目の例

慣性ナビゲーションの IMU と GPS フュージョン

この例では、無人航空機 (UAV) またはクワッドコプターに適した IMU と GPS のフュージョンアルゴリズムを作成する方法を示します。

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地上ビークルの位置と向きの推定

この例では、慣性測定ユニット (IMU) と全地球測位システム (GPS) 受信機からのデータを融合させて、地上ビークルの位置と向きを推定する方法を説明します。

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Ground Vehicle Pose Estimation for Tightly Coupled IMU and GNSS

Estimate the position and orientation of a ground vehicle by building a tightly coupled extended Kalman filter and using it to fuse sensor measurements. A tightly coupled filter fuses inertial measurement unit (IMU) readings with raw global navigation satellite system (GNSS) readings. In contrast, a loosely coupled filter fuses IMU readings with filtered GNSS receiver readings.

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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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Estimate Phone Orientation Using Sensor Fusion

MATLAB Mobile™ reports sensor data from the accelerometer, gyroscope, and magnetometer on Apple or Android mobile devices. Raw data from each sensor or fused orientation data can be obtained. This examples shows how to compare the fused orientation data from the phone with the orientation estimate from the ahrsfilter object.

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Visual-Inertial Odometry Using Synthetic Data

Estimate the pose (position and orientation) of a ground vehicle using an inertial measurement unit (IMU) and a monocular camera. In this example, you:

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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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Design Fusion Filter for Custom Sensors

Introduces how to customize sensor models used with an insEKF object.

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Estimate Orientation Using GPS-Derived Yaw Measurements

Define and use a custom sensor model for the insEKF object along with built-in sensor models. Using a custom yaw angle sensor, an accelerometer, and a gyroscope, this example uses the insEKF object to determine the orientation of a vehicle. You use the velocity from a GPS receiver to compute the yaw of the vehicle. Following a similar approach as shown in this example, you can develop custom sensor models for your own sensor fusion applications.

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