结合陀螺仪和加速度计数据 [英] Combine Gyroscope and Accelerometer Data
问题描述
我建立使用乐高Mindstorm的NXT系统平衡机器人。我使用从HiTechnic的两个传感器，首先是一个加速度计和第二个是一个陀螺仪。我已经成功地过滤掉噪声从两个传感器和来自角度为在-90和90度之间的范围内，0度是完全平衡的。
I am building a balancing robot using the Lego Mindstorm's NXT system. I am using two sensors from HiTechnic, the first being an Accelerometer and the second being a Gyroscope. I've successfully filtered out noise from both sensors and derived angles for both in a range between -90 and 90 degrees, with 0 degrees being perfectly balanced.
我的下一个挑战是两个传感器值结合起来，以纠正陀螺仪的漂移随着时间的推移。下面是我从实际的数据创建了一个例子图演示从陀螺仪漂移：
My next challenge is to combine both of the sensor values to correct for the Gyroscope's drift over time. Below is an example graph I created from actual data to demonstrate the drift from the gyroscope:
最常用的方法，我已经看到了，使结合这些传感器坚如磐石的是使用一个卡尔曼滤波器。不过，我不是微积分方面的专家，我真不明白的数学符号，我明白数学源$ C $ C虽然。
The most commonly used approach I've seen to make combining these sensors rock solid is by using a Kalman filter. However, I'm not an expert in calculus and I really don't understand mathematical symbols, I do understand math in source code though.
我使用RobotC（这就像任何其他C衍生物）和倒很AP preciate，如果有人可以给我如何完成这在C的例子。
I'm using RobotC (which is like any other C derivative) and would really appreciate if someone can give me examples of how to accomplish this in C.
感谢您的帮助！
解决方案结果：
好吧，kersny通过介绍我补充过滤器解决我的问题。这是说明我的结果的曲线图：
Alright, kersny solved my problem by introducing me to complementary filters. This is a graph illustrating my results:
结果＃1 的
结果＃2 的
正如你所看到的，过滤器校正陀螺漂移和两个信号合并成一个单一的光信号。
As you can see, the filter corrects for gyroscopic drift and combines both signals into a single smooth signal.
编辑：自从我反正固定破碎的形象，我认为这将是有趣的，以显示我用来产生这些数据的装备：
Since I was fixing the broken images anyways, I thought it would be fun to show the rig I used to generate this data:
推荐答案
卡尔曼滤波器是巨大的，但是我找到了互补滤波器更容易实现类似的结果。我已经找到了编码一个互补滤波器最理想的用品是<一href="http://web.archive.org/web/20091121085323/http://www.mikroquad.com/bin/view/Research/ComplementaryFilter"相对=nofollow>这个wiki （连同这篇文章关于此页面（在技术文档，相信在zip文件名是filter.pdf）;
Kalman Filters are great and all, but I find the Complementary Filter much easier to implement with similar results. The best articles that I have found for coding a Complementary Filter are this wiki (along with this article about converting sensors to Engineering units) and a PDF in the zip file on this page (Under Technical Documentation, I believe the file name in the zip is filter.pdf);
PS。如果你被困在一个卡尔曼滤波器， 是一些C语法$ C $下实现它的Arduino的。
PS. If your stuck on a Kalman Filter, here is some C-syntax code for the Arduino that implements it.
这篇关于结合陀螺仪和加速度计数据的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
