I have been struggling with how to fuse all the data into one cohesive mess. In the case of heading I have several source. I have a magnetometer, the heading from GPS,\u00a0the integrated value from the gyro, and if I can manage it, the solar compass.<\/p>\n
They each have strengths and weaknesses. The magnetometer is great as long as the car is not moving, the servos moving, or it sitting on a man hole cover.<\/p>\n
The GPS is great as long as the robot is moving a a reasonable speed and has a good lock.<\/p>\n
The gyro has no ability to determine north, but for short periods of time can keep track of changes in heading.<\/p>\n
The solar compass is fantastic as long as we have the sun and it is not too high in the sky.<\/p>\n
So what is a poor robot to do? One solution is to use a Kalman filter to merge the data. But the math is complex and you need\u00a0 mathematical model of your system to make best use of it. Now I have the habit of picking at everyone I know for knowledge (and even those I do not know). This has lead me to the complimentary filter.<\/p>\n
The complimentary filter is nothing more than a weighted average. Rather than each heading having equal weight when taking an average, you assign each a unique weight. The more likely that the value is right the higher the weight you give it.<\/p>\n
So how do you assign weights? The literature would have you use the parameter’s covariance. This is the inverse of the standard deviation. A parameter you have confidently measured would have a larger covariance than one you are not so sure about. There are lots of mathematical ways to determine covariance given sample sets of numbers. But in my case, I will take\u00a0 simpler way out.<\/p>\n
My weights will be the inverse of the expected accuracy of the system. Here are the cases:<\/p>\n
GPS: Zero if velocity is under 2 meters\/second, else it ramps up to 3 degrees at 6 m\/s or above.<\/p>\n
Magentometer: 5 degrees if motor is off, 180 degrees if on<\/p>\n
SUNDAR: Based on the error it reports based on the signal to noise ratio<\/p>\n
IMU: Starts with the accuracy of the device that last reset its heading. The accuracy then decreases by 90 degrees per minute before it gets reset by the best azimuth device.<\/p>\n
My heading now becomes the weighted average using the equation:<\/p>\n
heading = (h1*W1 + h2*W2 + h3*W3 + h4*W4)\/(W1+W2+W3+W4)<\/p>\n
The problem is this does not work for headings. To see why assume two equally weighted sensors providing the headings of 350 and 10 degrees. The average would be (350*1 + 10*1)\/(1+1) or 180 degrees! Oops; wrong direction! We really should have gotten 0 or 360 degrees.<\/p>\n
So how does one average headings? A future post will address that.<\/p>\n","protected":false},"excerpt":{"rendered":"
I have been struggling with how to fuse all the data into one cohesive mess. In the case of heading I have several source. I have a magnetometer, the heading from GPS,\u00a0the integrated value from the gyro, and if I … Continue reading