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I have an object moving with sinusoidal motion. I estimate the position of the object using lidar and camera separately. Then I want to fuse these two estimation data in the optimal way. For example I apply Kalman filtering to lidar data and I get estimated position and variance. This same process can be applied to camera data. I fuse these two using Weighted Average or Kalman Filters with Multiple Update Steps (as described here). This method can apply different kalman filter type as extended kalman filter, particle filter etc.

How can I apply different fusing methods or filtering methods to combine sensor data in a suitable way?

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  • $\begingroup$ What's the motivation for a different way? If the assumptions of Kalman filter hold in your case, stick with it. $\endgroup$
    – Mark
    Commented Dec 12, 2022 at 23:16
  • $\begingroup$ I want to compare between diffrent methods and I want to make a performance comparison between them. $\endgroup$
    – guidolard
    Commented Dec 14, 2022 at 5:43
  • $\begingroup$ dsp.stackexchange.com/questions/85434/… $\endgroup$
    – Mark
    Commented May 21, 2023 at 6:42

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