使用加速度计计算距离 [英] calculating distances using accelerometer
问题描述
在许多论坛上阅读了许多关于如何基于加速度数据
测量距离的研究和论文之后，我发现了双积分方法，但是与该方法有关的误差很大，并且随着时间的推移而增加。
另外，我发现有人建议卡尔曼滤波器，我阅读了一些有关它的参考资料，但我不清楚如何使用它。
也有一些人在谈论融合传感器...但是阅读它们之后，我没有得到任何新想法。
，所以我仍然很困惑，我没有找到正确的答案。...
对此冗长的介绍感到抱歉。
After reading many researches and papers beside to many forums about how to measure the distance based on the acceleration data I found the double integration method, but the error related to this method is big and increases by time. In addition I found some people who suggested Kalman filter I read some references about it but it was not clear to me how to use it. also some were talking about the fusion sensors ... but after reading them I did not get any new ideas. so I am still confused and I did not find the right answer to follow .... sorry for this long introduction.
问题
让我们考虑一下我手中握有9轴传感器并且我动了手从某个方向看，我如何才能找到手在空间中的新位置？如何获得从起始点到新点的运动矢量我的意思是如何知道三个轴上的通过距离？
Let us consider that I hold 9-axis sensor in my hand and I move my hand in some direction, how I can find the new position of my hand in the space? how to obtain motion vector from the initial point to the new point I mean how to know the passed distances on three axes?
如果没有直接答案...一些建议或参考会很棒，或者某些算法可以给出准确的答案，我可以自己研究和使用。
非常感谢
If there is no direct answer ... some advises or references would be great or some algorithms that give an accurate answers and I can study and use it by myself. thank you very much
推荐答案
您的问题的简短答案是您无法做到。
The short answer to your question is you can't do it.
双重积分方法实际上是仅使用加速度计来获取所需信息的唯一方法。您发现此方法有问题。误差会随着时间的推移而增加，通常不会提供许多人正在寻找的精度。
The double integration method is really the only way to get the information you are looking for using only an accelerometer. You found the problem with this method. The error increases over time and generally doesn't give the accuracy many are looking for.
卡尔曼滤波通常需要2个设备，并且基本上会同时兼顾设备和滤波器中的最佳性能不好。见下面的示例。
Kalman filtering usually requires 2 devices and basically takes the best of both devices and filters out the bad. See example below.
卡尔曼滤波是一个非常艰巨的主题，我试图深入进行高级设计，但是在有限的测试中却没有发现任何有意义的结果。 YouTube视频，是开始理解该主题的好地方系列。
Kalman filtering is a really tough subject that I tried to dive into for senior design, but never found any meaningful results with my limited testing. A great place to start understanding this subject is with this youtube video series .
这个人赢得了斯坦福大学DARPA挑战，并以一种易于理解的方式解释了这个话题。整个课程是一个由6个单元组成的视频系列，内容涉及编程机器人如何在未知环境中移动和了解它们的位置。
This is the guy that won the DARPA challenge with Stanford and explains the topic in an easy to understand way. The whole course is a 6 unit video series about programming robots to move and understand their location in an unknown environment. Worth a watch if you have the time and interest.
听起来像您在尝试做与我在高级设计中所做的类似的事情，以提供真正的相对位置。
It sounds like you're trying to do something similar to what I did for senior design in give really specific relative location information.
Another great Kalman filtering read this (if this link doesn't work google Kalman filter balance bot and click the TKJ blog link). Basically this guy uses an accelerometer and gyroscope to track orientation in the real world.
还有一些需要研究的内容 wiki 实时运动学。这在拖拉机上进行并结合在一起，以提供真正准确的位置信息。约翰·迪尔（John Deere）出售一套系统，但售价约2万美元。这是穷人的版本，使用 GPS和beagleboard
Something else to look into wiki Real Time Kinematic. This goes on tractors and combines to provide really accurate location information. John Deere sells a system, but it's like $20,000. Here is the poor man's version using GPS and beagleboard
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