Low‐cost implementation of a multi‐sensor fusion and PID control framework for quadcopter navigation using Raspberry Pi 3

Abstract This paper presents a low‐cost embedded implementation of a multi‐sensor fusion and proportional–integral–derivative (PID) control framework for quadcopter navigation using Raspberry Pi 3. The proposed modular cascaded architecture integrates lightweight Kalman and complementary filters with six PID controllers for full attitude and position control, achieving real‐time state estimation and control. The first stage employs Kalman filters to reduce inertial measurement unit noise, while the second stage uses complementary filters to combine accelerometer and gyroscope data for precise attitude estimation. Additional Kalman filters fuse accelerometer‐based predictions with GPS updates to enhance positioning accuracy under noisy conditions. The proposed system, which was developed in MATLAB/Simulink and implemented on a Raspberry Pi 3 device, was validated using processor‐in‐the‐loop testing. The results confirm the high accuracy, real‐time performance, and feasibility of onboard quadcopter navigation using low‐cost embedded hardware.