如何使用迭代最近点时,以纪念在点云库(PCL)NULL数据(ICP) [英] How to mark NULL data in Point Cloud Library (PCL) when using Iterative Closest Point (ICP)

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问题描述

I'm尝试调整2套点云中使用的迭代最近点(ICP)算法的点云中图书馆中集成(PCL)。 I'm得到一个错误报告说,它不能找到足够的对应点。我已经放宽了对参数的条件:setEuclideanFitnessEpsilon(-1.797e + 5),setMaximumIterations(40)和setRANSACIterations(2000年),仍然有同样的问题。(我还没有发现太多的信息哪些或如何将这些条件值应对于一个适当的alignement,所以任何帮助在这方面将是pciated太真AP $ P $)

I´m trying to align 2 sets of point clouds using the Iterative Closest Point (ICP) algorithm integrated within Point Cloud Library (PCL). I´m getting an error report saying that it cant find enough correspondence points. I have already relaxed the conditions for the parameters: setEuclideanFitnessEpsilon(-1.797e+5), setMaximumIterations(40) and setRANSACIterations(2000) and still having the same problem.. (I havent found much info about which or how these conditional values should be for a proper alignement, so any help in this regard would be really appreciated too)

I'm怀疑,这个问题已经做的事实,我在云中有许多NULL数据点,这从来就标有NULL值(0)。是,使用PCL如果使用得当?是否有PCL的空标准值?我清楚地不想算法试图对齐数据集时要考虑这些空点。

I´m suspecting that this problem has to do with the fact that I have many NULL data points in my cloud, which I´ve marked with the value NULL (0). Is that properly done when using PCL? Is there any NULL standard value for PCL? I clearly dont want the algorithm to consider those NULL points when trying to align the data sets..

感谢您的帮助

推荐答案

如果您正在使用PCL,无效数据的默认值是不为空,但为NaN。所以,如果你想标记点为无效,应首先包括<限制> 文件,然后设置岗位的std :: numeric_limits的:: quiet_NaN() 。它通常是这样的

If you are using PCL, default value of invalid data is not NULL, but is NaN. So if you want to mark a point as invalid, you should first include <limits> file and then set the positions to 'std::numeric_limits::quiet_NaN()'. It is usually done like this

const float bad_point = std::numeric_limits<float>::quiet_NaN();
if( is_invalid_point )
    p.x = p.y = p.z = bad_point;

但不管怎么说,配置ICP可以是一个痛苦。您可能需要做更多的参数调整取决于您的数据。

But anyway, configuring ICP can be a pain. You may have to do a lot more parameter tweaking depending on your data.

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