使用Android陀螺仪而不是加速度计。我发现了很多零碎的东西，但没有完整的代码

Question

Sensor Fusion视频看起来很棒，但没有代码：http://www.youtube.com/watch?v=C7JQ7Rpwn2k

这是我的代码，只使用加速度计和指南针。我还在3个方向值上使用了一个卡尔曼滤波器，但是这里显示的代码太多了。最终，这工作得很好，但是结果要么太不稳定要么太滞后，这取决于我对结果做了什么以及我把过滤因子设置得有多低。

/** Just accelerometer and magnetic sensors */
public abstract class SensorsListener2
    implements
        SensorEventListener
{
    /** The lower this is, the greater the preference which is given to previous values. (slows change) */
    private static final float accelFilteringFactor = 0.1f;
    private static final float magFilteringFactor = 0.01f;

    public abstract boolean getIsLandscape();

    @Override
    public void onSensorChanged(SensorEvent event) {
        Sensor sensor = event.sensor;
        int type = sensor.getType();

        switch (type) {
            case Sensor.TYPE_MAGNETIC_FIELD:
                mags[0] = event.values[0] * magFilteringFactor + mags[0] * (1.0f - magFilteringFactor);
                mags[1] = event.values[1] * magFilteringFactor + mags[1] * (1.0f - magFilteringFactor);
                mags[2] = event.values[2] * magFilteringFactor + mags[2] * (1.0f - magFilteringFactor);

                isReady = true;
                break;
            case Sensor.TYPE_ACCELEROMETER:
                accels[0] = event.values[0] * accelFilteringFactor + accels[0] * (1.0f - accelFilteringFactor);
                accels[1] = event.values[1] * accelFilteringFactor + accels[1] * (1.0f - accelFilteringFactor);
                accels[2] = event.values[2] * accelFilteringFactor + accels[2] * (1.0f - accelFilteringFactor);
                break;

            default:
                return;
        }




        if(mags != null && accels != null && isReady) {
            isReady = false;

            SensorManager.getRotationMatrix(rot, inclination, accels, mags);

            boolean isLandscape = getIsLandscape();
            if(isLandscape) {
                outR = rot;
            } else {
                // Remap the coordinates to work in portrait mode.
                SensorManager.remapCoordinateSystem(rot, SensorManager.AXIS_X, SensorManager.AXIS_Z, outR);
            }

            SensorManager.getOrientation(outR, values);

            double x180pi = 180.0 / Math.PI;
            float azimuth = (float)(values[0] * x180pi);
            float pitch = (float)(values[1] * x180pi);
            float roll = (float)(values[2] * x180pi);

            // In landscape mode swap pitch and roll and invert the pitch.
            if(isLandscape) {
                float tmp = pitch;
                pitch = -roll;
                roll = -tmp;
                azimuth = 180 - azimuth;
            } else {
                pitch = -pitch - 90;
                azimuth = 90 - azimuth;
            }

            onOrientationChanged(azimuth,pitch,roll);
        }
    }




    private float[] mags = new float[3];
    private float[] accels = new float[3];
    private boolean isReady;

    private float[] rot = new float[9];
    private float[] outR = new float[9];
    private float[] inclination = new float[9];
    private float[] values = new float[3];



    /**
    Azimuth: angle between the magnetic north direction and the Y axis, around the Z axis (0 to 359). 0=North, 90=East, 180=South, 270=West
    Pitch: rotation around X axis (-180 to 180), with positive values when the z-axis moves toward the y-axis.
    Roll: rotation around Y axis (-90 to 90), with positive values when the x-axis moves toward the z-axis.
    */
    public abstract void onOrientationChanged(float azimuth, float pitch, float roll);
}

我试图找出如何添加陀螺仪数据，但我就是做不对。http://developer . Android . com/reference/Android/hardware/sensor event . html上的google文档显示了一些从陀螺仪数据中获取增量矩阵的代码。这个想法似乎是，我会降低加速度计和磁传感器的滤波器，使它们真正稳定。这将跟踪长期的方向。

然后，我会从陀螺仪中保留最近的N δ矩阵的历史记录。每次我得到一个新的，我都会放下最古老的一个，把它们全部乘以得到一个最终的矩阵，我会乘以加速度计和磁性传感器返回的稳定矩阵。

这似乎行不通。或者，至少，我的实现是行不通的。其结果远比加速计的抖动大。增加陀螺仪历史记录的大小实际上会增加抖动，这让我觉得我没有从陀螺仪中计算出正确的值。

public abstract class SensorsListener3
    implements
        SensorEventListener
{
    /** The lower this is, the greater the preference which is given to previous values. (slows change) */
    private static final float kFilteringFactor = 0.001f;
    private static final float magKFilteringFactor = 0.001f;


    public abstract boolean getIsLandscape();

    @Override
    public void onSensorChanged(SensorEvent event) {
        Sensor sensor = event.sensor;
        int type = sensor.getType();

        switch (type) {
            case Sensor.TYPE_MAGNETIC_FIELD:
                mags[0] = event.values[0] * magKFilteringFactor + mags[0] * (1.0f - magKFilteringFactor);
                mags[1] = event.values[1] * magKFilteringFactor + mags[1] * (1.0f - magKFilteringFactor);
                mags[2] = event.values[2] * magKFilteringFactor + mags[2] * (1.0f - magKFilteringFactor);

                isReady = true;
                break;
            case Sensor.TYPE_ACCELEROMETER:
                accels[0] = event.values[0] * kFilteringFactor + accels[0] * (1.0f - kFilteringFactor);
                accels[1] = event.values[1] * kFilteringFactor + accels[1] * (1.0f - kFilteringFactor);
                accels[2] = event.values[2] * kFilteringFactor + accels[2] * (1.0f - kFilteringFactor);
                break;

            case Sensor.TYPE_GYROSCOPE:
                gyroscopeSensorChanged(event);
                break;

            default:
                return;
        }




        if(mags != null && accels != null && isReady) {
            isReady = false;

            SensorManager.getRotationMatrix(rot, inclination, accels, mags);

            boolean isLandscape = getIsLandscape();
            if(isLandscape) {
                outR = rot;
            } else {
                // Remap the coordinates to work in portrait mode.
                SensorManager.remapCoordinateSystem(rot, SensorManager.AXIS_X, SensorManager.AXIS_Z, outR);
            }

            if(gyroUpdateTime!=0) {
                matrixHistory.mult(matrixTmp,matrixResult);
                outR = matrixResult;
            }

            SensorManager.getOrientation(outR, values);

            double x180pi = 180.0 / Math.PI;
            float azimuth = (float)(values[0] * x180pi);
            float pitch = (float)(values[1] * x180pi);
            float roll = (float)(values[2] * x180pi);

            // In landscape mode swap pitch and roll and invert the pitch.
            if(isLandscape) {
                float tmp = pitch;
                pitch = -roll;
                roll = -tmp;
                azimuth = 180 - azimuth;
            } else {
                pitch = -pitch - 90;
                azimuth = 90 - azimuth;
            }

            onOrientationChanged(azimuth,pitch,roll);
        }
    }



    private void gyroscopeSensorChanged(SensorEvent event) {
        // This timestep's delta rotation to be multiplied by the current rotation
        // after computing it from the gyro sample data.
        if(gyroUpdateTime != 0) {
            final float dT = (event.timestamp - gyroUpdateTime) * NS2S;
            // Axis of the rotation sample, not normalized yet.
            float axisX = event.values[0];
            float axisY = event.values[1];
            float axisZ = event.values[2];

            // Calculate the angular speed of the sample
            float omegaMagnitude = (float)Math.sqrt(axisX*axisX + axisY*axisY + axisZ*axisZ);

            // Normalize the rotation vector if it's big enough to get the axis
            if(omegaMagnitude > EPSILON) {
                axisX /= omegaMagnitude;
                axisY /= omegaMagnitude;
                axisZ /= omegaMagnitude;
            }

            // Integrate around this axis with the angular speed by the timestep
            // in order to get a delta rotation from this sample over the timestep
            // We will convert this axis-angle representation of the delta rotation
            // into a quaternion before turning it into the rotation matrix.
            float thetaOverTwo = omegaMagnitude * dT / 2.0f;
            float sinThetaOverTwo = (float)Math.sin(thetaOverTwo);
            float cosThetaOverTwo = (float)Math.cos(thetaOverTwo);
            deltaRotationVector[0] = sinThetaOverTwo * axisX;
            deltaRotationVector[1] = sinThetaOverTwo * axisY;
            deltaRotationVector[2] = sinThetaOverTwo * axisZ;
            deltaRotationVector[3] = cosThetaOverTwo;
        }
        gyroUpdateTime = event.timestamp;
        SensorManager.getRotationMatrixFromVector(deltaRotationMatrix, deltaRotationVector);
        // User code should concatenate the delta rotation we computed with the current rotation
        // in order to get the updated rotation.
        // rotationCurrent = rotationCurrent * deltaRotationMatrix;
        matrixHistory.add(deltaRotationMatrix);
    }



    private float[] mags = new float[3];
    private float[] accels = new float[3];
    private boolean isReady;

    private float[] rot = new float[9];
    private float[] outR = new float[9];
    private float[] inclination = new float[9];
    private float[] values = new float[3];

    // gyroscope stuff
    private long gyroUpdateTime = 0;
    private static final float NS2S = 1.0f / 1000000000.0f;
    private float[] deltaRotationMatrix = new float[9];
    private final float[] deltaRotationVector = new float[4];
//TODO: I have no idea how small this value should be.
    private static final float EPSILON = 0.000001f;
    private float[] matrixMult = new float[9];
    private MatrixHistory matrixHistory = new MatrixHistory(100);
    private float[] matrixTmp = new float[9];
    private float[] matrixResult = new float[9];


    /**
    Azimuth: angle between the magnetic north direction and the Y axis, around the Z axis (0 to 359). 0=North, 90=East, 180=South, 270=West 
    Pitch: rotation around X axis (-180 to 180), with positive values when the z-axis moves toward the y-axis. 
    Roll: rotation around Y axis (-90 to 90), with positive values when the x-axis moves toward the z-axis.
    */
    public abstract void onOrientationChanged(float azimuth, float pitch, float roll);
}


public class MatrixHistory
{
    public MatrixHistory(int size) {
        vals = new float[size][];
    }

    public void add(float[] val) {
        synchronized(vals) {
            vals[ix] = val;
            ix = (ix + 1) % vals.length;
            if(ix==0)
                full = true;
        }
    }

    public void mult(float[] tmp, float[] output) {
        synchronized(vals) {
            if(full) {
                for(int i=0; i<vals.length; ++i) {
                    if(i==0) {
                        System.arraycopy(vals[i],0,output,0,vals[i].length);
                    } else {
                        MathUtils.multiplyMatrix3x3(output,vals[i],tmp);
                        System.arraycopy(tmp,0,output,0,tmp.length);
                    }
                }
            } else {
                if(ix==0)
                    return;
                for(int i=0; i<ix; ++i) {
                    if(i==0) {
                        System.arraycopy(vals[i],0,output,0,vals[i].length);
                    } else {
                        MathUtils.multiplyMatrix3x3(output,vals[i],tmp);
                        System.arraycopy(tmp,0,output,0,tmp.length);
                    }
                }
            }
        }
    }


    private int ix = 0;
    private boolean full = false;
    private float[][] vals;
}

第二块代码包含我对第一块代码的修改，第一块代码将陀螺仪添加到组合中。

具体来说，加速度的过滤因子变小(使值更稳定)。MatrixHistory类跟踪在gyroscopeSensorChanged方法中计算的最后100个陀螺仪deltaRotationMatrix值。

我在这个网站上看到了很多关于这个话题的问题。他们帮助我走到了这一步，但是我不知道下一步该怎么做。我真的希望传感器融合的人刚刚在某个地方发布了一些代码。他显然把所有的东西都放在一起了。

Answer 1

对于在哪里可以找到完整代码的问题，这里有一个Android jelly bean的默认实现:https://android.googlesource.com/platform/frameworks/base//JB-release/services/sensor service/Start通过检查fusion.cpp/h.它使用修改的Rodrigues参数(接近欧拉角)而不是四元数。除了定向之外，卡尔曼滤波器还可以估计陀螺漂移。对于测量更新，它使用磁力计和加速度(比力)，有点不正确。

要使用这些代码，你要么是一个巫师，要么了解INS和KF的基础知识。许多参数必须微调，以使过滤器工作。正如爱德华恰当地指出的那样，这些人以此为生。

至少在谷歌的galaxy nexus中，这个默认实现没有使用，被Invense的专有系统覆盖。

Answer 2

好吧，我要感谢你知道什么是卡尔曼滤波器。如果你愿意，我会编辑这篇文章，并给你我几年前写的代码，让你做你想做的事情。

但首先，我会告诉你为什么不需要它。

Android 传感器堆栈的现代实现使用传感器融合，如上所述 Stan。这只是意味着所有可用的数据 - 加速度，磁力，陀螺仪 - 都收集在一个算法中，然后所有输出都以Android传感器的形式读出。

编辑:我刚刚无意中发现了关于这个主题的精彩的谷歌技术演讲:Android设备上的传感器融合:运动处理的革命。如果你对这个话题感兴趣，45分钟的观看时间是值得的。

从本质上讲，传感器融合是一个黑匣子。我已经研究了Android实现的源代码，它是用C编写的一个大型卡尔曼滤波器。里面有一些非常好的代码，比我写过的任何过滤器都要复杂得多，而且可能比你正在写的要复杂得多。记住，这些家伙就是为了谋生。

我还知道，至少有一家芯片组制造商有自己的传感器融合实现。然后，设备制造商根据自己的标准在Android和供应商实现之间进行选择。

最后，正如Stan上面提到的，Invensense在芯片级有自己的传感器融合实现。

无论如何，归根结底，您设备中的内置传感器融合可能优于您或我可以拼凑的任何东西。所以你真正想做的是访问它。

在Android中，既有物理传感器，也有虚拟传感器。虚拟传感器是从可用的物理传感器合成的传感器。最着名的例子是TYPE_ORIENTATION它采用加速度计和磁力计并创建滚动/俯仰/航向输出。（顺便说一句，你不应该使用这个传感器，它有太多的限制。

但重要的是，较新版本的Android包含这两个新的虚拟传感器：

TYPE_GRAVITY是过滤出运动影响的加速度计输入TYPE_LINEAR_ACCELERATION是过滤出重力分量的加速度计。

这两个虚拟传感器通过加速度计输入和陀螺仪输入的组合进行合成。

另一个值得注意的传感器是TYPE_ROTATION_VECTOR，它是由加速度计、磁强计和陀螺仪合成的四元数。它表示设备的全三维方向，并过滤掉线性加速度的影响。

然而，对于大多数人来说，四元数有点抽象，因为您可能无论如何都要处理三维变换，所以您的最佳方法是通过sensor manager . getrotationmatrix()将TYPE_GRAVITY和TYPE_MAGNETIC_FIELD结合起来。

还有一点:如果你正在使用一个运行旧版本Android的设备，你需要检测到你没有接收到TYPE_GRAVITY事件，而是使用TYPE _ ACCELEROMETER。理论上，这将是一个使用自己的卡尔曼滤波器的地方，但如果你的设备没有内置传感器融合，它可能也没有陀螺仪。

无论如何，这里有一些示例代码来展示我是如何做到的。

  // Requires 1.5 or above

  class Foo extends Activity implements SensorEventListener {

    SensorManager sensorManager;
    float[] gData = new float[3];           // Gravity or accelerometer
    float[] mData = new float[3];           // Magnetometer
    float[] orientation = new float[3];
    float[] Rmat = new float[9];
    float[] R2 = new float[9];
    float[] Imat = new float[9];
    boolean haveGrav = false;
    boolean haveAccel = false;
    boolean haveMag = false;

    onCreate() {
        // Get the sensor manager from system services
        sensorManager =
          (SensorManager)getSystemService(Context.SENSOR_SERVICE);
    }

    onResume() {
        super.onResume();
        // Register our listeners
        Sensor gsensor = sensorManager.getDefaultSensor(Sensor.TYPE_GRAVITY);
        Sensor asensor = sensorManager.getDefaultSensor(Sensor.TYPE_ACCELEROMETER);
        Sensor msensor = sensorManager.getDefaultSensor(Sensor.TYPE_MAGNETIC_FIELD);
        sensorManager.registerListener(this, gsensor, SensorManager.SENSOR_DELAY_GAME);
        sensorManager.registerListener(this, asensor, SensorManager.SENSOR_DELAY_GAME);
        sensorManager.registerListener(this, msensor, SensorManager.SENSOR_DELAY_GAME);
    }

    public void onSensorChanged(SensorEvent event) {
        float[] data;
        switch( event.sensor.getType() ) {
          case Sensor.TYPE_GRAVITY:
            gData[0] = event.values[0];
            gData[1] = event.values[1];
            gData[2] = event.values[2];
            haveGrav = true;
            break;
          case Sensor.TYPE_ACCELEROMETER:
            if (haveGrav) break;    // don't need it, we have better
            gData[0] = event.values[0];
            gData[1] = event.values[1];
            gData[2] = event.values[2];
            haveAccel = true;
            break;
          case Sensor.TYPE_MAGNETIC_FIELD:
            mData[0] = event.values[0];
            mData[1] = event.values[1];
            mData[2] = event.values[2];
            haveMag = true;
            break;
          default:
            return;
        }

        if ((haveGrav || haveAccel) && haveMag) {
            SensorManager.getRotationMatrix(Rmat, Imat, gData, mData);
            SensorManager.remapCoordinateSystem(Rmat,
                    SensorManager.AXIS_Y, SensorManager.AXIS_MINUS_X, R2);
            // Orientation isn't as useful as a rotation matrix, but
            // we'll show it here anyway.
            SensorManager.getOrientation(R2, orientation);
            float incl = SensorManager.getInclination(Imat);
            Log.d(TAG, "mh: " + (int)(orientation[0]*DEG));
            Log.d(TAG, "pitch: " + (int)(orientation[1]*DEG));
            Log.d(TAG, "roll: " + (int)(orientation[2]*DEG));
            Log.d(TAG, "yaw: " + (int)(orientation[0]*DEG));
            Log.d(TAG, "inclination: " + (int)(incl*DEG));
        }
      }
    }

六羟甲基三聚氰胺六甲醚；如果您手边正好有一个四元数库，那么只需接收TYPE_ROTATION_VECTOR并将其转换为数组可能会更简单。