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An EKF or any of the variants of the Kalman filter, as you said mainly works in two steps: prediction and correction. The prediction steps gives you a state estimate based on your process model and the correction step updates your state estimate based on the current measurement. If you have multiple measurements from more than one sensor, you would just ...


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There are many possible routes you can take. You mentioned CAN. CANOpen is built on CAN and is used in many automation and robotics related devices. CANOpen specifies a SYNC mechanism exactly for the purpose you mention. More about this here. As for cycle times, the bandwidth and exact message size of CAN packets is knows, so cycle times are easy to ...


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No (for best outcome) it is not enough. As you might have observed, the error that Gyro produces during stationary position is very random and has no specific order. No matter how much reading you take for average offset, you cannot eliminate it. One solution is simply to use sophisticated sensor which is not always possible. Another solution is to use ...


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1) Before I go into filter design itself, I want to know do I just use the raw data from sensor ' as it is ' and feed it into filter system ? Or Do i need to some sort of preprocessing on data for filter to use ? Both happens. In my applications(Visual-Inertial SLAM) we tend to just use the raw measurements. Note that the fusion algorithm(Kalman filters or ...


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I can propose you two solutions to solve your problem: The first one is a not easy to implement with a MEMS but it works: A Kalman filter (maybe more a Extended Kalman filter for navigation). The advantage of this method is that it can gives you the covariance for each state of your model. The disavantage is that you need to implement the model of the ...


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Yes. Example of this can be found here. Depending on how good your modeling is you could also use the IMU to help detect wheel slippage.


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When you have one fast-moving source and want to fuse it with a slow-moving source, a complimentary filter should be sufficient. Hopefully, it's a lot easier to understand than Kalman filters. There are plenty of examples where they use a complimentary filter to combine accelerometer and Gyroscope. When you say the Z-Axis, I assume you mean vertical axis ...


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Find extrinsic between the sensors using a calibration target or any other methods. Now that you know the relative locations of the sensors, transform the points in each sensor to the first LiDAR coordinate(e.g top left LiDAR in your figure). All the points are merged into the first LiDAR coordinate now. You need to learn 1. rigid transformation of 3d ...


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Sensor fusion is a process where you are gathering information from one or more sensors and inputting that data to your processor to process the data to make sense whether it is useful or not. In your case, you have a stereo camera, IMU and GPS. If your question is whether all of these sensors altogether will increase the accuracy of the map, then the ...


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A followup on holmeski's reply. It seems those pages have been taken down. I found the old webpages on the 'Way Back Machine'. Pixhawk Attitude Estimator (EKF) Pixhawk ekf_att_pos_estimator (Application)


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