基于STM32F103的，MPU6050,MS5611,HMC5883三个传感器读取，总共10轴数据，全部读取和单独读取皆可，测试完美通过，可串口显示。一切在网上的传播需经本人同意。

MPU6050的C代码程序，MPU6050的C代码程序（包括DMP直接解算姿态+匿名上位机显示姿态）

相机位姿估计1：根据四个特征点估计相机姿态 随文Demo 使用opencv基于特征点估计位姿

9轴姿态传感器 BST BNO055 AN007-00 快速入门指南 汉化版

BMX055九轴姿态传感器模块 包含调试通过的K60 KEA128硬件软件IIC 四元数姿态解算程序，包含调试上位机以及说明文档。做恩智浦和飞思卡尔平衡车的可以参考。大自然的搬运工。

基于STM32F103C8T6的四轴飞行器飞控源码，采用MPU6050加速度计陀螺仪，滑动平均滤波，采用四元数姿态解算，欧拉角计算，两级PID进行控制，角速度环加角度环控制，实现四轴飞行器平稳飞行，程序通俗易懂，希望帮助大家学习~

在姿态确定的四元数方程基础上，采用扩展卡尔曼滤波

入门阶段必看，基本都有注释，包括速度更新，姿态更新，位置更新

用IMU的数据进行机器人位置和姿态的估计，比如acc或者gyro积分每个sample怎么进行坐标变换，怎么由rawdata得到位置和姿态信息的计算细节等。 In recent years, microelectromechanical system (MEMS) inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors. We discuss dierent modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and ltering as well as computationally cheaper extended Kalman lter and complementary lter implementations. The quality of their estimates is illustrated using both experimental and simulated data.

用matlab模拟人行走，跑动。所需的数据已经存在，直接可以运行看到效果。