Wonderful lecture sir! Thank you so much. How does one decide which technique of SLAM is appropriate for an application , given that there are many new techniques like RatSLAM etc too ?
My solutions to the lab exercises can be found here github.com/conorhennessy/SLAM-Course-Solutions. Have a look at my read me, there you will find other solutions that I have found online
great explanation :) , i think if you had include the exercise of the lecture it will more and more helpful , any way to get the second part of the lecture , exercise part 🤔
if you are looking for exercises, here it is, on the official page of the course you can find it: ais.informatik.uni-freiburg.de/teaching/ws12/mapping/
@@padraopv absolutely no worries! I thought I would share them with the world. If you have any questions at all reply here or raise an issue on GitHub!
We can represent rotation, scaling etc using matrix multiplication. But translation still is an addition operation. With H.C. translation can also be represented as matrix multiplication.
Cyrill Stachniss Thanks for your quick reply. I am a PhD researcher. My study is multidisciplinary research, and I need to know more about photogrammetry which i have seen in the aforementioned link you teach those what i need. May i know that you have also tutorials or videos on those as well ?
everybody who makes a tutorial on homogeneous coordinates only tells you what are the advantages of homogeneous coordinates but then doesnt actually explain what they physically mean because "its not part of the course". what a waste of thirty miutes.
I think he pretty much explained it around 9:54 the best he could, any physical mean would need a real life application and it is different in each and every case, this is only math! To put it in a real life example: if you think O3 is your focal point, and the plane (defined here by w=1) is your plane of projection (basically your CCD camera sensor) then u, v stands for the pixel position of your CCD sensor... w will be 0 as you have no depth information as it is explained at 11:50.
While still a good introduction, NJWILDBERGER will explain what they mathematically mean. Watch his videos and you will begin to start seeing why they are neccesary. And how you can begin to think about them naturally.
This was great! Made me understand the transformation matrices we used in the euler lagrange framework much better :)
Wonderful lecture sir! Thank you so much.
How does one decide which technique of SLAM is appropriate for an application , given that there are many new techniques like RatSLAM etc too ?
Can be based on indoor or outdoor application, full SLAM or online SLAM, and dense or sparse mapping.
Great revision before starting my thesis :-) thanks Prof Cyrill.
Did u do phd in SLAM mam?
everything is amazing, except the colour of the pointer, i can't see it at all lol, however, god bless you for sharing this immense knowledge
Ok i just found the new versions :) Thanks alot great work
The explanation might have been ok , but it was definitely incomplete. Did not explain what exactly a homogenous transformation was.
For that I think you need to look into linear mapping in linear algebra, which I believe is a prerequisite of this course.
Thanks dr.cyrill
Informative video!! Thanks Prof Cyrill
Is it possible to get the new Slides of the viedeos? I only found the older version of it on the Uni Freiburg homepage.
Great lecture Prof
Excellent teaching
Inverting matrices is an expensive process. Does that matter?. Perhaps not, for no more than twelve row/column matrices.
It is constant number of dimensions, so don't care...
where can I find solutions to the assignments for cross check?
My solutions to the lab exercises can be found here github.com/conorhennessy/SLAM-Course-Solutions. Have a look at my read me, there you will find other solutions that I have found online
crystal clear. thanks
great explanation :) , i think if you had include the exercise of the lecture it will more and more helpful ,
any way to get the second part of the lecture , exercise part 🤔
if you are looking for exercises, here it is, on the official page of the course you can find it: ais.informatik.uni-freiburg.de/teaching/ws12/mapping/
Might be helpful - my solutions to the lecture exercises can be found here github.com/conorhennessy/SLAM-Course-Solutions
@@UrbanPretzle Thank you very much for that!
@@padraopv absolutely no worries! I thought I would share them with the world.
If you have any questions at all reply here or raise an issue on GitHub!
can we get access to the lab content ?
@@urbanfps9080 Thank you so much!
My solutions to the lab exercises can be found here github.com/conorhennessy/SLAM-Course-Solutions
WHY on the Earth i need these "homogeneous coordinates" ???
We can represent rotation, scaling etc using matrix multiplication. But translation still is an addition operation. With H.C. translation can also be represented as matrix multiplication.
Can i have your presentation slides. It is really impressive your tutorial.
Yes, see: www.ipb.uni-bonn.de/slides-slam/
Cyrill Stachniss Thanks for your quick reply. I am a PhD researcher. My study is multidisciplinary research, and I need to know more about photogrammetry which i have seen in the aforementioned link you teach those what i need. May i know that you have also tutorials or videos on those as well ?
The link to the slides are no longer valid. Could you supply us with a valid link? Thanks.
这里有ais.informatik.uni-freiburg.de/teaching/ws13/mapping/
@@tongxingjin8152 thanks a lot
Where to code slam?
Is it possible to integrate slam with ros without using its packages
yo , great
everybody who makes a tutorial on homogeneous coordinates only tells you what are the advantages of homogeneous coordinates but then doesnt actually explain what they physically mean because "its not part of the course". what a waste of thirty miutes.
I think he pretty much explained it around 9:54 the best he could, any physical mean would need a real life application and it is different in each and every case, this is only math!
To put it in a real life example: if you think O3 is your focal point, and the plane (defined here by w=1) is your plane of projection (basically your CCD camera sensor) then
u, v stands for the pixel position of your CCD sensor... w will be 0 as you have no depth information as it is explained at 11:50.
While still a good introduction, NJWILDBERGER will explain what they mathematically mean. Watch his videos and you will begin to start seeing why they are neccesary. And how you can begin to think about them naturally.