STM32-Based IMU Attitude Estimation

This project involves using an STM32 microcontroller and an MPU6050 sensor module to measure angular velocity and acceleration. The collected sensor data will be processed to determine the attitude angles (roll, pitch, and yaw) of the system. The project will include sensor calibration, data filtering (e.g., complementary or Kalman filter), and real-time angle computation for accurate motion tracking applications.

Introduction

This project aims to build a simple but functional system that can estimate the orientation (attitude) of an object in real time. We used an STM32F103C8T6 microcontroller together with an MPU6050 sensor, which includes a 3-axis gyroscope and a 3-axis accelerometer.

By collecting data from the sensor and processing it through our own code, we can estimate how the object is tilted—specifically its roll, pitch, and yaw angles. To make the results more stable and accurate, we applied filtering techniques (e.g., complementary filter) to combine acceleration and gyroscope data.

The project mainly involves:

Reading raw sensor data through I2C communication

Calibrating the sensor to reduce offset and noise

Using a filtering algorithm to compute stable angle values

Updating the angles in real time

This kind of system is commonly used in applications like drones, mobile phones, and robot balance systems. Our goal is to understand how such a system works from both a hardware and software perspective by building one from scratch.

Team Members

LI DING

FAN XUTING

Project Overview

The idea of this project is to estimate the orientation (attitude) of an object using sensor data. We used the MPU6050 module, which combines a gyroscope and an accelerometer, to measure how the object is moving and rotating in space. The sensor data is collected using the STM32F103C8T6 microcontroller through I2C communication.

Since the raw data from the sensor is not very stable or accurate by itself, we applied a filtering method (in our case, a complementary filter) to combine the gyroscope and accelerometer readings. This allows us to reduce noise and drift, and get smoother and more reliable angle values over time.

The final goal is to calculate the roll, pitch, and yaw angles of the system in real time. This kind of setup is commonly used in things like self-balancing robots, drones, camera stabilizers, or even mobile phones for detecting screen orientation. Our project is a small-scale version of such systems, built from scratch to better understand how attitude estimation actually works.

Setup

Equipment

Here are the main components used in this project:

STM32F103C8T6 ("Blue Pill") – A versatile microcontroller for controlling the system.

MPU6050 – A 6-axis IMU sensor that includes both a 3-axis accelerometer and a 3-axis gyroscope.

USB to TTL Serial Converter – Used for programming the STM32 and communicating with the serial terminal.

Jumper wires and breadboard – For making connections between the STM32 and MPU6050.

Power supply (3.3V) – Provided by the STM32 or through an external 3.3V regulator.

The MPU6050 module is powered with 3.3V, and it communicates with the STM32 over the I2C bus.

Wiring / Circuit Overview

Here's how the components are connected:

MPU6050 Pin	STM32 Pin	Notes
VCC	3.3V	Powered by STM32
GND	GND	Common ground
SDA	PB7	I2C data line
SCL	PB6	I2C clock line

The sensor is connected to the STM32 using the I2C interface, which allows the STM32 to read the sensor's data. I2C is a simple and efficient communication protocol for embedded systems.

Optional: A USB to TTL converter is used for programming the STM32 and debugging through the serial port. This allows us to print the sensor data or estimated angles to the terminal.