#Opensourcedrone #Drones #ROS #complementary filtering #UAVdevelopment #VisionbasedUAV #Opensource #Drone #FMT #PX4

Recently, FMT author Echo introduced the general steps of porting C/C++ algorithm to FMT in the article "FMT C/C++ Algorithm Porting Steps Explained", using the porting of PX4's EKF navigation algorithm as an example. In order to further verify the working of EKF, we conducted a comparison test of two navigation algorithms, EKF and complementary filtering.
Thanks again also to @mooncakeG for his contribution to the porting of the EKF algorithm to FMT. See the video below for details of the test.

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About Amovlab:

Founded in 2016, Amovlab provides open source hardware and software tools and courses for mobile robotics R&D. Our main goal is to make R&D more efficient. Through the operation of the technology community, we have formed the Prometheus autonomous drone open source project and the supporting P series drone hardware devices. The project is paired with a variety of peripheral intelligent solutions, such as indoor/outdoor formation, pod visual tracking, and stepped education solutions. Also supporting simulation host, vision/depth sensors, high-bandwidth communication devices and other peripheral spare parts. Finally, we have established a trinity of open source robotics projects, hardware and software development tools, educational solutions and community services. At the same time, adhering to the concept of "doing the hard but right thing", we will gradually focus on the field of robot design visualization and simulation software to further improve the efficiency of robot development.

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