iOS：3D 空间中的运动精度
据我了解，GPS 的精度有限。是否有可能在 3D 空间中获得人的更精确位置？
我计划编写一个应用程序，用户可以将手机向任何方向移动，但只需要移动几英尺。是否有任何技术（也许使用加速度计和陀螺仪）可以实现这种精度？
例如，用户将手机平放（以便手机底部与地板平行）。屏幕上绘制了一些东西，当人向前、向后和左右行走时，它不会“随着屏幕”移动。因此，该物体看起来是不可移动的，只有将手机置于 3D 空间中的正确位置才能发现。
我不需要任何人来编写一些代码，只要给我一个简短的了解，如果通过一些努力，这甚至是可能的。
谢谢！
德里克
From what I understand, GPS has limited accuracy. Is it possible to get a more precise location for a person in 3D space?
I'm planning to write an application where users will move with their phones in any direction, but only need to move a few feet. Is there any technique, maybe with the accelerometer and gyroscope that would enable this degree of accuracy?
For example, the user holds the phone flat (so that the bottom of the phone runs parallel with the floor). There is something drawn on-screen, which doesn't move "with the screen" as the person walks forward, backward, and side-to-side. Thus, the object appears to be unmoveable, and only discoverable by having the phone in the correct position in 3D space.
I don't need anyone to lay out some coding, just give me a brief understanding if, with some hard work, this could even be possible.
Thanks!
Derek
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不，不是真的。如果你将加速度计值积分两次，你就可以得到位置，但误差是可怕的。在实践中是没有用的。
以下是 23:20 的原因说明（Google 技术讲座）。
您实际上可以做的是通过更改手机的方向来发现“隐藏的对象”。仅使用加速度计和指南针就可以很好地工作。这正是您在 colechristensen 发布的视频中看到的内容。
No, not really. If you integrate the accelerometer values twice you get position but the error is horrible. It is useless in practice.
Here is an explanation why (Google Tech Talk) at 23:20.
What you actually could do is to discover your "hidden object" by changing the orientation of the phone. That works pretty well with just accelerometers and compass. This is exactly what you see in the video posted by colechristensen.
看看这个
通过使用“传感器融合”组合来自各个传感器的输出，可以显着增加位置。我认为最有趣的概念是使用相机的输出来检测和记录运动（与陀螺仪、加速度计和 GPS 结合）。您应该编写一个卡尔曼滤波器来组合。
问题是，这是一个花了数年时间研究这些东西的工程研究生的水平。并非不可能，但要做好迎接挑战的准备。
Look at this
It is possible to significantly increase the location by combining the outputs from the various sensors using "sensor fusion". The most interesting concept, I think, is using the output from the camera to detect and record movement (combining with the gyros, accelerometers, and gps). You should write a Kalman Filter to combine.
The problem is that this is on the level of an engineering grad student who has spent years studying such things. Not impossible, but be prepared for a challenge.