卡尔曼滤波+解算姿态
时间: 2024-01-26 20:02:23 浏览: 49
卡尔曼滤波是一种用于估计系统状态的算法,它结合了传感器测量值和系统模型的预测值,以提供更准确的状态估计。在姿态解算中,卡尔曼滤波可以用于将陀螺仪和加速度计的测量值结合起来,以估计物体的姿态(即旋转角度)。
以下是一个使用卡尔曼滤波解算姿态的示例代码:
```c++
#include <Wire.h>
#include <MPU6050.h>
#include <Kalman.h>
MPU6050 mpu;
Kalman kalmanX;
Kalman kalmanY;
double accX, accY, accZ;
double gyroX, gyroY, gyroZ;
double gyroXangle, gyroYangle;
double compAngleX, compAngleY;
double kalAngleX, kalAngleY;
uint32_t timer;
void setup() {
Serial.begin(9600);
Wire.begin();
mpu.initialize();
mpu.dmpInitialize();
mpu.setDMPEnabled(true);
kalmanX.setAngle(0);
kalmanY.setAngle(0);
timer = micros();
}
void loop() {
while (!mpu.dmpReady) {
// 等待DMP初始化完成
}
mpu.resetFIFO();
while (mpu.getFIFOCount() < 42) {
// 等待数据读取完成
}
mpu.getFIFOBytes(fifoBuffer, packetSize);
mpu.dmpGetQuaternion(&q, fifoBuffer);
mpu.dmpGetGravity(&gravity, &q);
mpu.dmpGetYawPitchRoll(ypr, &q, &gravity);
accX = ypr[2] * 180 / M_PI;
accY = ypr[1] * 180 / M_PI;
accZ = ypr[0] * 180 / M_PI;
gyroX = mpu.getRotationX();
gyroY = mpu.getRotationY();
gyroZ = mpu.getRotationZ();
gyroXangle += gyroX * (micros() - timer) / 1000000;
gyroYangle += gyroY * (micros() - timer) / 1000000;
compAngleX = 0.93 * (compAngleX + gyroX * (micros() - timer) / 1000000) + 0.07 * accX;
compAngleY = 0.93 * (compAngleY + gyroY * (micros() - timer) / 1000000) + 0.07 * accY;
timer = micros();
kalAngleX = kalmanX.getAngle(accX, gyroX, (micros() - timer) / 1000000);
kalAngleY = kalmanY.getAngle(accY, gyroY, (micros() - timer) / 1000000);
Serial.print("Kalman X: ");
Serial.print(kalAngleX);
Serial.print("Kalman Y: ");
Serial.println(kalAngleY);
}
```
这段代码使用了Arduino和MPU6050陀螺仪模块。它通过读取陀螺仪和加速度计的测量值,并使用卡尔曼滤波算法来估计物体的姿态。最后,通过串口输出估计的姿态角度。