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관성 센서 융합

IMU 및 GPS, 센서 융합, 사용자 지정 필터 조정을 통한 관성 항법

관성 센서 융합에서는 IMU, GPS, 기타 센서의 측정값을 개선하고 결합하기 위해 필터를 사용합니다. 특정 센서를 모델링하려면 센서 모델 항목을 참조하십시오.

동시적 위치추정 및 지도작성에 대한 내용은 SLAM 항목을 참조하십시오.

함수

모두 확장

ahrsfilterOrientation from accelerometer, gyroscope, and magnetometer readings
ahrs10filterHeight and orientation from MARG and altimeter readings
complementaryFilterEstimate orientation using complementary filter
ecompassOrientation from magnetometer and accelerometer readings
imufilterOrientation from accelerometer and gyroscope readings
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
insfilter관성 내비게이션 필터 만들기
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
tunerPlotPosePlot filter pose estimates during tuning

블록

AHRSOrientation from accelerometer, gyroscope, and magnetometer readings
Complementary FilterEstimate orientation using complementary filter

도움말 항목

센서 융합

응용 분야