You are welcome - great to hear that the course helps understanding the material!

I have never seen such a wonderful course!

this feels like a mix of a well researched and produced tutorial and my actualy lectures at university :D love it

This is exactly what I was looking for. Thank you Universitat Frieburg.

Thank you Cyrill for these uploads! I am a Masters student doing a dissertation only project and your videos are saving me so much time and confusion! Really great.

Thank you sir!! Please continue to spread knowledge on your platform

you are a life saver :) perfectly explained, thank you so much for recording this video, i was thinking there will be no lecture on SLAM but fortunately i found it. Thankyou again.

As soon as the questions are focused and precisely formulated, no problem.

Thank you so much for the uploads. Feels so good to be getting my attention span back while watching your lectures! Hope I keep coming across and learning from amazing minds like you.

i took this course to finish my mater thesis about slam, i was using a kinect camera to map my university.

No, the environment itself does not change so there is no time dependency as for the x, z, or u.

Excellent video. Now, when I was studying SLAM using Probabilistic Robotics I was a bit taken aback about the fact that odometry was actually perceived as control. But when one looks close at it, odometry is only reporting what the robot thinks it has done, which is basically what you told it to do (control). It basically just translates your commands into some calculation supposing that internal parameter (such as wheel diameter) are correct.

Thanks for this uploads. They are a big help for me to get into the basics of SLAM. I have read most of your papers but with this videos, explain step by step, its much easier to get it. Thank you very much.

At 18:00, shouldn't the grey star be below the gold star(the star below the word same). Because in its wrong path the distance of the star from the robot measured by the robot is less than the actual distance so, in estimation of the star's position from the robot's path(the real one through which it should go) should be less. so, it should be nearer.

I found it very useful and thanks a lot for posting lectures on SLAM and explaining it in so detail.

I just watched the ekf and ukf videos, they were great. Now I'm going to watch the entire course. Thanks a lot for making the video available!

A well explained SLAM lecture. Thank you teach.

Simply wow. Thank you very much for sharing. (from the other part of the world : Sri Lanka)

I am in 2nd Year of B.Tech course ! Can you suggest prior Mathematical knowledge for completing this course or list of prerequisites . I need to learn SLAM for an upcoming project on Autonomous Quadcopter !

Thank you so much this is an amazing course and it is rarely to find an excellent demonstration about that topic

Sir i have a doubt? in this tutorial you explained us how slam works and mapping but you given everything as LA derivations i have doubt here that how to convert these derivations in to real-time coding in python or in C++ to make practically possible,because i'm still learning and exploring...

You are great tutor. Thank you for sharing your knowledge with everyone without any expectations.

Thanks, awesome introduction. My professor tends to skip the basics!

Beautifully done!

Okay, Sir Cyril what if you don't want wire linings as demarcation. Can the SLAM method still be durable. Or what other methods of mapping and localization helps a lawn mower possibly mower an area effectively.

A nice source to learn SLAM.

Yeah they really do! I'll going to watch all of your Courses, hope it's ok to ask if there are any questions about the material :)

Perfectly Explained. So i was currently working with dynamic SLAM problem. Could you suggest which sensors can be more effective in dense dynamic environment?

Thanks so much. It's very informative and easy to approach SLAM :)

SLAM words of full form Releted with safety.

14:58 hmm but there is no landmark...🤔 that was so fun and nice expression for measurement update 😂😂 thank you for these awesome lectures!

Nice lessons Sir. Great work

this is actually amazing, i have a question though, if i were to use model predictive control to minimize navigation errors, how could i approach that ? and how about fault tolerant control, do you have any example of applying it for navigation ? my system is a differential drive, thank you in advance

This is quite helpfull, you have my thanks.

Hi Mr. Stachniss! Thanks for this video-lectures! I am new in this area (SLAM). I am trying to implement SLAM in a car robot with a four ultrasonic and a stereo camera. I having difficulties choosing and adapting one of the slam algorithms to this particular case. Could you give some advice about it?

That mint is scary. what if it sends out mappings of your house to dubious people. now they know your house inside and out. it would be a serious IoT issue. nice course btw. Thank you!

Thank you very much for spreading knowledge ❤️ Are the slides of these course available to recall them from time to time?

thank you so much for the uploads ! this is really great ! thank you :D

come on and slam, and welcome to the jam

it was great lecture.

Okay... Where's your Patreon page at? My Mobile Robotics lecturer at Warwick could learn a thing or two from your lecture quality.

thank you very much , this is what im looking forrrrrr

Thanks Sir.

Excellent course and equally understandable teaching. Thank you very much!

ok, im fine with that. Just thought that since m represent the map we obtain from the measurements, it change or let me say its getting bigger for every timestep we do. So that z(t) depends on a other map then then z(t-5) and so on. But i guess the map we obtained at (t-1) looks different then at (t) but its still the same map/environment we working with. Thanks for the Help.

If the landmarks are very distinctive, why not triangle between those and the vehicle. As they did 500 years ago.

Nice one Cyrill

Where can I find these slides? It will be really great if you could add it's link in the description

Excellent primer!

Great explanation! Thanks!

This is amazing =) Great Job

1:15:00 - Summary

Thanks for this uploads.

Could you please tell if we can get a copy of these nice slides somewhere?

state estimation vs localization?

Dear professor Cyrill Stachniss, I am an undergraduate student of China. I am writing to ask would you please offer me some information about where can I buy the book you use in this course? I am really interested in SLAM. Thank you.

wish you taught at my uni....

this is just awesome

cool uploads.

Brilliant

This what i want!

that's exactly what i need! how to download it?

Thank you, sir, for these lectures. I'm doing a project on SLAM, and I have some questions about slam. can you please help me out to solve those problems? (Lots of love from INDIA)

Thanks!

thanks you so much .

Thank you

32:11 Definition of SLAM Process

Estate Estimation 00:03:10

Can you please share course materials?

is there a way to get the slides?

Nice!

Are we just going to ignore the fact that this guy has undergone great fat loss transformation now and looks ripped AF in 2021

Do we allways just have one map? Should not on the Graphical Model for every timestep a map. Just like the observations z(t-1), z(t)...z(T) there is a map m(t-1), m(t)...m(T)?

it would be better with subtitle

Danke

thank you for sharing

It know where is its because it know where it isnt

51:58 Việt Nam điểm danh :3

I hope that the people who have disliked this video get a place in heaven :p

b