why the function predict can be used in 'Factor Graph-Based Pedestrian Localization with IMU and GPS Sensors', but the same data cant be used in prcdict function example

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世怡 荆
世怡 荆 2024-3-18
回答： Aditya 2024-6-25
Why the function predict can be used in 'Factor Graph-Based Pedestrian Localization with IMU and GPS Sensors', but the same data cant be used in prcdict function example. I try to use the acce and gyro reading data from 'Factor Graph-Based Pedestrian Localization with IMU and GPS Sensors' into predict function's example,however, the result is different and also has a big difference. I want to know the reason. Waiting for your answers! thanks a lot!!!

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回答（1 个）

Aditya
Aditya 2024-6-25
The discrepancy you're observing between the results of the predict function when using data from the "Factor Graph-Based Pedestrian Localization with IMU and GPS Sensors" example and another example can be attributed to several factors. These include differences in data preprocessing, sensor fusion algorithms, and the underlying assumptions of the models.
Here is a general outline of steps you can follow to preprocess the data and align it with the requirements of the predict function:
  • Load and Preprocess Data
% Load IMU and GPS data
imuData = load('imuData.mat'); % Replace with actual data loading
gpsData = load('gpsData.mat'); % Replace with actual data loading
% Synchronize timestamps
imuTimestamps = imuData.timestamps;
gpsTimestamps = gpsData.timestamps;
% Resample data if necessary
[imuResampled, gpsResampled] = synchronizeData(imuData, gpsData);
  • Set Initial Conditions
% Define initial state and covariance
initialState = [initialPosition; initialVelocity; initialOrientation];
initialCovariance = eye(9); % Adjust based on your model
  • Define Sensor Noise Characteristics
% Define noise characteristics for accelerometer, gyroscope, and GPS
accelNoise = 0.01; % Example value, replace with actual
gyroNoise = 0.01; % Example value, replace with actual
gpsNoise = 1.0; % Example value, replace with actual
  • Run Prediction
% Create a state transition function for the predict function
stateTransitionFcn = @(state, controlInput) yourStateTransitionFcn(state, controlInput);
% Run the predict function
[predictedState, predictedCovariance] = predict(stateTransitionFcn, initialState, initialCovariance, imuResampled, gpsResampled, accelNoise, gyroNoise, gpsNoise);

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