Wonderful Thanks for the lecture

You can show and explain the exmple first and then tell the corredponding conceepts. For example, when you introduce the Integral image, it is more nuature to show page 50 and then page 49. I can not catch up when you talk much solely on the P49 utill I see the P50. Or you can conbine the P49 and P50 into one page. But anyway, this is a good course and help me much! Thanks.

This is by far the best explaination of this topic. Thank you!

Ive been going on the treadmill and busting these open. Ive learned a ton towards my SfM implementation

老师太厉害了，我受益匪浅，课程内容非常形象生动，让我深刻地理解到了ICP算法，谢谢老师的分享精神，帮了我很大的忙

老师讲得特别细致，在国内几乎没有这样细致的讲解，非常感谢你，让我解决了困扰我很久的概念问题

Great lecture with perfect explanations, much appreciated!

8:27 The reason why use the homogeneous coordinate instead of the cartesian -

I think the main take away is that everything is Gaussian and stays Gaussian!

Will this work for a planar lidar?

Thank you professor :)

real teacher at work. Thank you from switzerland

Hello Cyrill, do you have a presentation about spacial desuniform computation to measure the growth of the plants?

Спасибо❤

This is an excellent and informative presentation. It is one of the clearest, most accurate and complete treatments of the kinematics of the various wheeled vehicle configurations that I have seen. Once one understands the kinematics, it is straightforward to arrive at a kinematic analysis of other configurations. (Omni wheels, for example.)

Thank you!

very clear explained，thanks

Reinforcement learning lecture is coming?😊

These are life savers

nice!

Amazing work! Hats off to you good sir, concise explanation of a very complex topic

Hallo, are the slides for this lecture available online.

is Graph slam only full (offline) slam or can it be online too?

where can I find the lecture note?

How do I preprocess the NGSIM dataset and implement vehicle trajectory prediction in Python?

Thank you for sharing sir. I have a question: why is the complexity O(k^2.4 + n^2)? I understand that O(k^2.4) comes from the matrix inversion, but where does O(n^2) come from? Why isn't it something like O(n^2 * k + n * k^2) due to matrix multiplication?

As a prospective PhD this is a rare gem and the channel in its entirety !!!

What is the size of the model that performs this? How fast does it run?

The time when 3D data will be actively used in generative models seems to be approaching.

Excellent 👌

Too good - I wish I had studied your videos 10 years ago when I was starting out. Somehow, the books don't give an intuitive picture making this a much more difficult area to approach than it should be. Prof. Stachniss, you should write a book with some good pen and paper and programming exercises. Forstner is probably the best right now. (I work in self driving cars, on BEV modelling, and LOVE this subject).

Thank you, amazing explanation. 👍

An outstanding lecture on template matching both in theoretical and in pratical.

So is the initial input to both the KF and EKF the gaussian pdf functions or what else?

Very your explanation! Thank you! You did not mentioned accuracy in your video, could you explain how accuracy is calculated in this kind of model? The math behind isn’t clear for me.

Impressive work, could you please provide GitHub repository for reproducibility?

There are unknown way to visualize subspace, or vector spaces. You can stretching the width of the x axis, for example, in the right line of a 3d stereo image, and also get depth, as shown below. L R |____| |______| TIP: To get the 3d depth, close one eye and focus on either left or right line, and then open it. This because the z axis uses x to get depth. Which means that you can get double depth to the image.... 4d depth??? :O

I wish to apply this practically. I have only thought of the graph implementation.

Really nice ! What is the cheapest LiDaR sensor chip for 3D indoor Mapping ?

TNQ

on a slide 18 (computing alpha_t). isn't alpha_t = argmin(alpha) sum(pa(alpha)) ?

Great Work👍

So clearly explained! Thank you.

Thank you Professor.

Great work, Matteo! Cheers from Boulder

Hello, I enjoyed watching your video. And I have one question. In radar mapping, if you use distance thresholding like that, wouldn't newly observed objects not be mapped?

No

Amazing

I noticed LiDAR would most likely be something that is installed on top of a passenger car like a 'TAXI' sign, but that kind of appearance would not aesthetically compromise a TaxiBot design value. Couple of missing pieces in achieving autonomous level-5 practicallity would probably be : • multi weather urban road application under various kind of debris flying around the LiDAR 3D scope detection while in motion, and (but not limited to) • Black Box auto-pilot system capturing these events as & when the car is mobile on the road without time limiting factor on data memory captured. Do you reckon that a level-5 autonomous TaxiBot well equiped with Cameras + Radars + LiDARs + Black Box (w/ unlimited data time captured via cloud) ready to be rolled out on Chinese road (if citing for example Huawei level-5 software) ?

Thank you for this video.