God! I was waiting for one lecture about point clouds. I feel so fortunate to have access to these lectures. Enlightening! Thanks to Prof. Cyrill for the lecture.

this shows the best side of the internet: vast possibilities to broaden one's horizon of knowledge through simply accessible means -- thank you for this uploading this comprehensive lecture!

During my PhD study, I wished to have such an easy and simple way of explaining point cloud alignment. Indeed, I know ICP since years, however, this is the easiest way to explain and comprehend ICP algorithm. I strongly recommend this video, which will save your time and boost your knowledge.

@CyrillStachniss

3 жыл бұрын

Thank you

Thank you for making these videos available!!! I will, however, point out one small issue that I noticed - there's a bunch of static noise scattered throughout the video (around 27:34 mark, for example).

@CyrillStachniss

3 жыл бұрын

Sorry for that. I will pu a re-recording on my todo list.

@weiheng134

3 жыл бұрын

@@CyrillStachniss Hi Prof. Cyrill, you can add a Noise Filter to the audio perhaps! Very very good video btw!

Tremendous thanks Prof. Stachniss.. Your effort in presenting & providing such well explained materials to the public is incredible.

@CyrillStachniss

2 жыл бұрын

Thanks

quite intuitive, thanks for sharing! loved your lecture

Thanks for the video, all is more clear now!

Really nice. Thanks for sharing this

Such a great lecture! Sad the audio goes bad around the 23 minute mark!

@CyrillStachniss

2 жыл бұрын

Simply look to the next years version

Do you have recommendations for further reading regarding the background of the Orthogonal Procrustes Problem? I was unable to find the Soderkvist source mentioned on the slide.

@yousofebneddin7430

4 жыл бұрын

Isn't it this link: www.ltu.se/cms_fs/1.51590!/svd-fitting.pdf

ICP might work perfectly with just translation since it uses closest point approach. What happens in case of complex translation involved. Closer point go further away and far away points come close by. Then the corresponding points will be totally opposite.

Hi. Thanks for sharing these videos. Could you put them in a playlist? It is hard to find the sequence. Thanks

@andrzejreinke

4 жыл бұрын

It's in the playlist: kzread.info/dron/i1TC2fLRvgBQNe-T4dp8Eg.htmlplaylists called: SSE2 - Sensors and State Estimation Course (2020)

@CyrillStachniss

4 жыл бұрын

The are - the Sensors and State Estimation 2 Course Playlist on my channel

This is amazing. I do have some questions though. 1) In 15:39, would it matter if we did the full translation and then rotation? Also wouldn't this method of iteratively trying to align the points lead to error accumulation and in the end completely mess up the final result? Kindly let me know.

Hi Cyrill. I couldn't get the idea of subtracting center of mass. If we do so, the point cloud will be overlapping only when they are in same coordinate space, and as per my understanding, we use ICP to get R & t between two different coordinate spaces. Correct me if I'm wrong anywhere.

@user-gj8wm7ne2p

3 жыл бұрын

Not 100% sure if he means this, but imagine, you take a scan in the center of a circle. Then you moved north by a bit, you take another scan. Even though the translation is not known, from the two scan from the body frame, there will be center of mass difference of each frame at their respective body frame. By subtracting the com, you can estimate the translation between the two scan.

@Prof. Cyrill, if not too much to ask, could we access the homeworks?

@CyrillStachniss

3 жыл бұрын

See: www.ipb.uni-bonn.de/html/teaching/exercises-2020/2020-stachnisslab-all-exercises.zip

I have a doubt. Does anyone know how we get the correspondences C here. If I have two sets of data, how do I know which term in first corresponds to which term in second set. Thank you.

Thanks alot for these videos. is it possible to get the slides of this course too ?

@H3MAESSAM

4 жыл бұрын

slides are here www.ipb.uni-bonn.de/msr2-2020-2/

@vlogsofanundergrad2034

3 жыл бұрын

@@H3MAESSAM Page link invalid...

How to get the right center of mass, when you have noise? I would expect some error here. Just using the correspondent points sounds impossible at that step because if we know these we would alredy have our solution?

@CyrillStachniss

3 жыл бұрын

That is typically not the limiting factor. You are averaging over a larger set of points, so at least if you have no bias (zero mean noise), the effect should is minimal.

Thanks for your video, it explains great. But I have a problem while scanning data to enter the ICP algorithm, that is I read the frame data every 300ms (including depth data). Before the data is put in to merge into one, its capacity is full of RAM and leads to application stops. You or anyone who has a solution to this problem, please help me, please (the language I use C# and C++)

Data association near 34:09

There is a noise at 23:04, please remove it