Re: PCL-users Digest, Vol 83, Issue 35

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Re: PCL-users Digest, Vol 83, Issue 35

Biswajit Biswas
Good suggestion and really helpful..

On Thu, Jul 27, 2017 at 10:30 PM, <[hidden email]> wrote:
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Today's Topics:

   1. Re: Registration crash with big PointCloud (theilerp)


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Message: 1
Date: Wed, 26 Jul 2017 23:49:48 -0700 (MST)
From: theilerp <[hidden email]>
To: [hidden email]
Subject: Re: [PCL-users] Registration crash with big PointCloud
Message-ID: <[hidden email]>
Content-Type: text/plain; charset=UTF-8

Hi Tom,

I already answered you directly to your email, but for completeness, I will
post the answer here as well.

Regarding your problem using K-4PCS - namely the memory consumption and as
described in your email the bad results – there seems to be a basic usage
problem and some parameter issues.

K-4PCS intends to register pointclouds represented by keypoints - BUT the
keypoints need to be generated BEFOREHAND and PASSED as input to the K-4PCS
registration class. The keypoint extraction is not part of the PCL K-4PCS
implementation. The suggested workflow to extract useful keypoints is:

- In case of static scans, first apply a voxel-grid (or uniform-sampling)
filter. The leaf size (sampling distance) should thereby be set to keep as
much information as possible while making the point density more uniform
within the region of interest (i.e., overlapping area). In my scenarios (mid
range laserscans), leaf sizes between 0.01m and 0.5m worked well. If your
input pointcloud is already quite uniform, you can skip this.

- Extract 3D keypoints from the (uniformed) pointcloud. The goal of this
step is to strongly reduce the pointcloud while keeping points which are by
their nature well suited for registration. You should try to extract
minimally 1K and maximally 10K keypoints (if it is an easy scenery, less are
also okay; if it is a very complex scenery, a couple more are also okay, but
try to avoid it, you already realized that the algorithm becomes super slow
with more than 20K points).

- Note, both steps can easily be accomplished using PCL methods (e.g. use
VoxelGrid and SIFTKeypoint).

Finally, let’s have a look at the user parameters:

- Make sure your test PC is good enough to handle 10 parallel threads - it
does not make sense to use more threads than available CPU cores + the more
threads, the higher the memory consumption.

- Estimated overlap of 0.8 (= 80%) sounds good

- The delta value is quite critical (but sadly not very well documented,
neither for 4PCS nor for K-4PCS). “Delta” can be used as a relative value
which is then further used as a weight for calculating further parameters
such as expected distance to neighbors (as for example in the standard
4PCS). For K-4PCS it should not and cannot be set as a relative value,
because the keypointcloud density is not a useful value to derive further
parameters from (e.g. because keypoints can be quite clustered). Hence
“Delta” needs to be set absolute (in meters). The good news is, it still
makes sense to set it according to the density of the (uniformed) pointcloud
which served as basis for the keypoint extraction, because this value is a
good estimation of the keypoint “accuracy”. If you have used a voxelgrid
filter, the leaf size can directly be used as “Delta” (e.g. 0.05m),
otherwise you should calculate the approx. pointcloud density yourself if
you do not know it in advance (there are PCL methods for it, e.g. in the
4PCS implementation).

- The score threshold of 0.15 is okay. If not set by the user, it is
automatically set to 1 – estimated overlap (which would be 0.2 in your
case). If you don’t want to early terminate, set the value to 0.

I hope these hints help you and I would be interested to hear about your
results




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End of PCL-users Digest, Vol 83, Issue 35
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thanks and regards 
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