@adnanfahad6526
I can see how much effort has been put into this video. Great explanation!!! Thanks a lot.
@ibadrather
I did udacity Robotics Software Engineering Nanodegree. and studied a lot about SLAM. BUt this explanation beats everything. wow.
@xephyr417
I love you, Brian Douglas. You have been teaching me for so many years.
@AndreyGominiuk-qp7kk
Probably the best video I watched in the past few months on YouTube so far, not only on the slam topic, but in general ❤
@vaneesh03
These Tech Talks are GOLD!
@talhayousuf4599
Ah! Brian Douglas! I learnt all control systems concepts from you, by your videos I was able to integrate equations with real world, you are wonderful at teaching. Allah bless you
@alfascanerllc3786
Very useful stuff, really academic and easy to comprehend at the same time. Thanks.
@snackbite5360
dude im very grateful for the resourceful explanation. thank you so much, this is what future of education will look like. bless you all who reads this xoxo
@mikeharville7203
Awesome video, with excellent visual illustrations! However: It seems to imply that you create a loop closure link when you somehow know (from external means) that the robot is in the exact same place as before. My understanding is that what people actually do is identify matching features (or sets of features) between measurements at two different but similar robot locations, estimate a relative pose between those that would satisfy the observed changes (e.g. image locations for visual features, or angles and distances for lidar features) in the matched features in the two measurements, and then add that relative pose as the new link in the graph. It would be good (maybe in a followup video) to go into that more, and also into how one actually optimizes the pose graph, which again would involve some matching/alignment between features or other measurements at linked poses.
@jiayonglau
Thank you for your effort! Very underrated video!!
@bluecpp
Thank you for these great introductory videos to the topic. 👍
@yazankayyali6458
the effort put into this video is SO APPRECIATED, thanks !
@georgederleres8489
It's funny how my MSc in Robotics teaches SLAM by throwing a bunch of math equations without any attempt for an intuitive understanding of the problem at first. So thank you Bryan, for visualizing what the problem with realistic pose estimation and measurements is and the explanation that followed !
@orhirshfeld
Always love to learn from you, you make it easy to learn
@r.h.9043
excellently explained and visualized!
@apppurchaser2268
Amazing man thank you so much for clean and concise explanations
@vishvraval4079
Great explanation
@parthd714
who dared to dislike?
@alhdlakhfdqw
thank you so much for your perfect video!
@prandtlmayer