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 (R2019b 以降)
`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 (R2022a 以降)
`insOptions`	Options for configuration of `insEKF` object (R2022a 以降)
`insAccelerometer`	Model accelerometer readings for sensor fusion (R2022a 以降)
`insGPS`	Model GPS readings for sensor fusion (R2022a 以降)
`insGyroscope`	Model gyroscope readings for sensor fusion (R2022a 以降)
`insMagnetometer`	Model magnetometer readings for sensor fusion (R2022a 以降)
`insMotionOrientation`	Motion model for 3-D orientation estimation (R2022a 以降)
`insMotionPose`	Model for 3-D motion estimation (R2022a 以降)
`insCreateMotionModelTemplate`	Create template file for motion model (R2022b 以降)
`insCreateSensorModelTemplate`	Create template file for sensor model (R2022b 以降)
`positioning.INSMotionModel`	Base class for defining motion models used with `insEKF` (R2022a 以降)
`positioning.INSSensorModel`	Base class for defining sensor models used with `insEKF` (R2022a 以降)

フィルター調整

`tunerconfig`	Fusion filter tuner configuration options (R2020b 以降)
`tunernoise`	Noise structure of fusion filter (R2020b 以降)
`tunerPlotPose`	Plot filter pose estimates during tuning (R2021a 以降)

ブロック

AHRS	Orientation from accelerometer, gyroscope, and magnetometer readings (R2020a 以降)
Complementary Filter	Estimate orientation using complementary filter (R2023a 以降)
IMU Filter	Estimate orientation using IMU Filter (R2023b 以降)
ecompass	Compute orientation from accelerometer and magnetometer readings (R2024a 以降)

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 customize 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.
Autonomous Underwater Vehicle Pose Estimation Using Inertial Sensors and Doppler Velocity Log
This example shows how to fuse data from a GPS, Doppler Velocity Log (DVL), and inertial measurement unit (IMU) sensors to estimate the pose of an autonomous underwater vehicle (AUV) shown in this image.

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).
Tilt Angle Estimation Using Inertial Sensor Fusion and ADIS16505
Get data from Analog Devices ADIS16505 IMU sensor and use sensor fusion on the data to compute the tilt of the sensor. (R2024a 以降)
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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センサーフュージョンを使用した電話の向きの推定

MATLAB Mobile™ は、Apple または Android のモバイルデバイスの加速度計、ジャイロスコープ、および磁力計からのセンサーデータをレポートします。各センサーからの生データまたは融合した向きのデータを取得できます。この例では、電話からの融合した向きのデータをahrsfilterオブジェクトからの向きの推定と比較する方法を示します。

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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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