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I was looking for some comparison between these two approaches, but couldn't find any. I am wondering, what are the actual differences in terms of power consumption, accuracy, convergence speed and complexity of implementation and tuning between them? Why do most quadcopter firmwares use Kalman filtering for sensor fusion?

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Make sure you use your duckduckgoing a little harder...finding comparisons between fusion algorithms is pretty easy.

A comparison between Madgwick, Kalman, and Complimentry filters is easy to find.

Reading individual papers for each fusion method will give you specific answers to each method.

Which one is better is mostly depends what you have for sensor data. Madgwick typically uses 9dof sensors, while Kalman algorithms i‘ve seen with 6dof.

Comp filters I‘ve seen with just 2.

Why quads typically use Kalman is anyones guess besides the persons who implemented, however its likely they arn‘t using 9dof sensors, and don‘t bother with madgwick.

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    $\begingroup$ The main advantage of the referenced paper is, that the question how to solve the sensor fusion problem is ignored. Instead the reader gets entertained with a description of bilinear transformations and a full rotation matrix. This ensures, that the reader will ask a lot of question which can only be answered by the university professor who doesn't share his knowledge with the audience in advance. $\endgroup$
    – Manuel Rodriguez
    Commented Oct 26, 2019 at 9:53
  • $\begingroup$ The link is also dead. $\endgroup$
    – ZachS
    Commented Mar 9, 2023 at 21:54
  • $\begingroup$ I'll fix it at some point with a new google search. $\endgroup$
    – DrMrstheMonarch
    Commented Mar 10, 2023 at 16:34
  • $\begingroup$ That article mainly discusses 1D (single axis) data fusion, and only mentions 3D data fusion at the very end. There, it then disregards Kalman filters as "much more complicated" and the Madgwick filter as "not appropriate for 6DOF IMUs" (although they work very well in that area), and discusses only the Mahoney filter. While it is sure an interesting read, it provides absolutely no "comparison between Madgwick, Kalman, and Complimentry filters" as you suggest. $\endgroup$
    – Fritz
    Commented May 10, 2023 at 11:54

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