用于汽车跟踪路径的卡尔曼滤波器
我有一组点，例如 Point(x,y)。当汽车在同一条路上走了这么多路之后，它几乎弄乱了生成的地图。我听说卡尔曼滤波器可以从可用路径中创建一条路径。
有谁能说一下如何制作吗？我不是计算机科学专业的。请向我解释一下这个概念和那些矩阵。然后我会对它们进行编码。请任何人告诉我这个概念。
I am having a set of Points like Point(x,y). After the car gone through so many ways in the same road it is almost messing the resulting map. I heard that Kalman filter can make a sigle path from the available paths.
Can any body say how to make it? I am not from computer science. So please explain me about that concept and those matrices. Then I will code them. Please anybody enlighten me about the concept.
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据我所知，卡尔曼滤波器能够组合相同信息的多个来源，以获得对观测变量的更精确的测量。还可以与多次测量的同一测量装置组合。
这是一个很好的介绍：INTRO，另一个
As far as I know is the Kalman filter capable to combine several sources of the same information to get a more precise measurement of the observed variable. It could be possible to combine also with the same measruement device measured multiple times.
Here is a good introduction:INTRO, AnotherOne
我不知道这个问题是否仍然有效，但如果您想了解有关卡尔曼滤波器的更多信息，我强烈推荐 这个简短的 matlab 脚本。即使您没有安装 matlab，它也应该是您可能找到的最简单的卡尔曼示例！
I don't know if this question is still active, but if your intressted in learning more about the Kalman filter I can strongly recomend this short matlab script. Even if you dont have matlab installed it should be about the simplest example on Kalman your likely to find!
我不明白卡尔曼滤波器在这里是如何应用的。
我会通过图像处理来解决这个问题，这样粗路径就会变成细线，或者通过路径段上的连续线性回归来解决。
I don't see how exactly a Kalman filter would be applied here.
I would approach this problem either by image processing, so a thick path would be reduced to a thin line or by successive linear regression on the path segments.
当没有可用的路线图信息时，您可能尝试使用检测到的汽车坐标来确定道路在哪里。在没有路的时候尝试去开辟一条路，对吗？
卡尔曼滤波器旨在平滑从传感器获得的值。当传感器检测到汽车时，传感器可能不会给出汽车的实际位置。它将包含一些 x 和 y 坐标错误。
当从传感器获取数据时，您必须将这些 x,y 值输入卡尔曼滤波器。或者至少按照从传感器获取的正确顺序。
卡尔曼滤波器将为您提供 x 和 y 位置的估计值（平滑值），这将告诉您汽车的大致正确位置。
假设汽车在道路中间行驶，这些估计（过滤后）的 x,y 值就是您可以将其视为道路中点的值。
我现在才看到你的问题。我知道已经晚了，但我希望这有帮助吗？
You're probably trying to use the detected coordinates of a car, to determine where a road is, when there is no roadmap information available. Trying to create a road when there is no road, right?
The Kalman Filter is meant to smoothen values obtained from a sensor. When a sensor detects a car, the sensor may not give the car's actual position. It will contain some errors in x and y coordinates.
You have to feed these x,y values to the Kalman filter while the data is being obtained from the sensor. Or at least in the correct order that it was obtained from the sensor.
The Kalman filter will give you the estimated values (smoothened values) of x and y positions, which will tell you approximately the correct position of the car.
Assuming that the car is travelling in the middle of the road, these estimated (filtered) x,y values are what you can take as the midpoints of the road.
I saw your question only now. I know it's late, but I hope that helped?