Talk:STM32-Based IMU Attitude Estimation - Revision history

Liding: /* Team Members */

2025-04-05T06:57:31Z

Team Members

← Older revision		Revision as of 14:57, 5 April 2025
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	~~==Team Members==~~

Liding: /* Introduction */

2025-04-05T06:57:26Z

Introduction

← Older revision		Revision as of 14:57, 5 April 2025
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	~~== 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==		==Team Members==

Liding at 02:25, 5 April 2025

2025-04-05T02:25:54Z

← Older revision		Revision as of 10:25, 5 April 2025
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			== 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.		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.

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	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.		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==

Liding: Created page with "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..."

2025-04-05T02:25:01Z

Created page with "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..."

New page

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.