Hey! Excellent visuals and quality of video. Maybe the style of video isn't for me, but I certainly got lost (in this video and your later ones). I'm a stats / ML 3rd year student. Is the addition of additional information, the correction factor, or is it the cyclical fashion of the two parts conjoined, or is it the amount of belief we put in the sensor data? I did not have a clear grasp on what physical quantity corresponded to what abstract symbol in the derivation, and when we did get the derivation, I didn't understand what it showed us. I'm now really interested in the subject! There seems to be so much cool stuff here, the presentation just seems to have lost me. Will keep an eye on your channel for sure
Hey @mkwTL! Thanks for reaching out and providing some feedback :) I'm not 100% sure I understand the question but it seems like you're asking about what the sensor data is. The sensor data is often quantities like lidar measurements or distances to known objects. So @time=29s in the video, the yellow arrow is the sensor data. In localization, by knowing where the object is relative to me (in the localization problem we assume a known environment, but if we don't know the environment and need to estimate that as well then that is a SLAM problem which will come in a future video) I can generate an estimate of where I am. But in addition to just sensor information, if I know how I travel, I can use that information to determine where I am. So the idea behind the Bayes Filter is combining both of these types of information to generate a probabilistic estimate of where we are. I'm glad you're interested in this topic! Let me know if you have any questions or want to actually chat about it! My discord is Orblics#4757
Nice video! At 2:45, should the instances of p(y_k | ..., y_{1:k}) be p(y_k | ..., y_{1:k-1})?
Yes! Thank you! Great catch; I'll pin so others can see it too :)
super neat and intuitive. Thanks!
One of the best explanations 💪 Great video, thanks!
Thank you! I really appreciate it :)
Hey! Excellent visuals and quality of video. Maybe the style of video isn't for me, but I certainly got lost (in this video and your later ones). I'm a stats / ML 3rd year student. Is the addition of additional information, the correction factor, or is it the cyclical fashion of the two parts conjoined, or is it the amount of belief we put in the sensor data? I did not have a clear grasp on what physical quantity corresponded to what abstract symbol in the derivation, and when we did get the derivation, I didn't understand what it showed us.
I'm now really interested in the subject! There seems to be so much cool stuff here, the presentation just seems to have lost me. Will keep an eye on your channel for sure
Hey @mkwTL! Thanks for reaching out and providing some feedback :)
I'm not 100% sure I understand the question but it seems like you're asking about what the sensor data is. The sensor data is often quantities like lidar measurements or distances to known objects. So @time=29s in the video, the yellow arrow is the sensor data. In localization, by knowing where the object is relative to me (in the localization problem we assume a known environment, but if we don't know the environment and need to estimate that as well then that is a SLAM problem which will come in a future video) I can generate an estimate of where I am. But in addition to just sensor information, if I know how I travel, I can use that information to determine where I am. So the idea behind the Bayes Filter is combining both of these types of information to generate a probabilistic estimate of where we are.
I'm glad you're interested in this topic! Let me know if you have any questions or want to actually chat about it! My discord is Orblics#4757