Understanding Sensor Fusion and Tracking, Part 1: What Is Sensor Fusion?
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- เผยแพร่เมื่อ 6 มิ.ย. 2024
- Check out the other videos in the series:
Part 2 - Fusing an Accel, Mag, and Gyro to Estimation Orientation: • Understanding Sensor F...
Part 3 - Fusing a GPS and IMU to Estimate Pose: • Understanding Sensor F...
Part 4 - Tracking a Single Object With an IMM Filter: • Understanding Sensor F...
Part 5 - How to Track Multiple Objects at Once: • Understanding Sensor F...
This video provides an overview of what sensor fusion is and how it helps in the design of autonomous systems. It also covers a few scenarios that illustrate the various ways that sensor fusion can be implemented.
Sensor fusion is a critical part of localization and positioning, as well as detection and object tracking. We’ll show that sensor fusion is more than just a Kalman filter; it is a whole range of algorithms that can blend data from multiple sources to get a better estimate of the system state. Four of the main benefits of sensor fusion are to improve measurement quality, reliability, and coverage, as well as be able to estimate states that aren’t measure directly. The fact that sensor fusion has this broad appeal across completely different types of autonomous systems is what makes it an interesting and rewarding topic to learn.
Check out these other references!
Kalman Filter Tech Talk Series: bit.ly/2pnEA6a
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May God bless the author, the creator, the supporters, everyone who has contributed for the generation of this video. Thanks a lot!
Really you are the boss in this field,
Welcome back 👌
Very interesting series. Im working on a car project using acceleration, velocity, location and camera vision. Sensor noise is one of the issues. so i will follow the series. Thanks
Thanks :) I was waiting for this series in sensor fusion and kalman filtering
Brilliant. Absolutely brilliant. You're a life saver mate.
"Where am I? What am I doing? And what state am I in?" Are questions I often ask myself when I wake up hungover.
Hi Brian, thanks for taking the time to make the videos, they're very helpful. But I was wondering if you could provide me with some references to the subject of system Identification methods (books or videos). Thank you in advance.
Great explanation, amazing insights!
Thank you, Sir! I get you at 100%.
Excellent Video! Thanks!
A+ would watch again
brian thank you so much i am following control theory lessons and i hope there are more videos on sensor fusion topic
There are 5 videos on sensor fusion and tracking. The 3rd will post tomorrow and then the last two early next week.
Thank You!!
perfect video!
Thank you!
Hi Brian, Can you suggest me some textbook about this topic. Thank you !
I might have arrived late to this video, but how does the author do the drawings? What software is he using? TY!
Well-Done
hello sir, Can you guide me in topic of Localization of underwater AUV using kalman filter?
cool video
Please add the link for Kalman filter series.
ThankYou
Yep, looks like all of the reference links were left off :( I'll ask MATLAB to add them back in. Thanks for letting me know!
Does the IMM filter effect on SNET calculations?
can you please introduce the articles this video is based on?
Hello Brian
really appreciate your video, it's really awesome
also I need your recommendation, please
my professor refused my advice to use the Kalman filter for fusion IMU sensors because it's an old algorithm. and since the update is one of our research scopes, I have to fully prove that Kalman is the best in light of computation and time or I have to find an alternative algorithm. so please recommend me to answer, is Kalman is the best (there is particle filter i think it works for fusion) and how to proof (it's good if supported by paper's referenced) or what algorithm is suited, especially our research target is the localization and tracking the object.
thanks for your sharing knowledge
Any one have an idea 👆🏻👆🏻.plz
@@abdullahal-hashar we can talk about this.
Hey Brian! tnx as always! Is there a possibility that you can give us a map or flowchart of the control engineering branch? I've been working in this field for 2 years, using methods like LQR, Bang-Bang, Fuzzy and etc and I'm still a bit dizzy when it comes to explaining it simply or making a big picture of it. Some divide it into classic and modern, or intelligent and non-intelligent.
Again thanks for simplifying the concepts!:)
It was hard for me also to find a mental structure that can help make sense of the entire field of control engineering. Other divides could be model-free or model-based control, frequency or time domain, discrete or continuous, suboptimal or optimal, nonlinear or linear, and variable structure or fixed structure. I'm working on a way that I think describes everything in a convenient and understandable way (hopefully, anyway!). It's probably a few months out still.
@@BrianBDouglas wow great! ok thank you very much👍
What software did you use for this style of video? Was it photoshop recorded with OBS?
Hey Cody, I wrote up my process here engineeringmedia.com/my-setup
Hi Brian
I want the image map of control engineering
Where to i download
Hi Shesha, I assume you're talking about the one that I have on my website? That is part of a talk I give on an overview of all of control engineering. Every time I give the talk I tweak that image a bit. Once it stops evolving I'll make a video on it and distribute the map to whoever wants it.
Brian Doublas is THE Salman Khan (from Khan Academy) of Control System Theory
Tuyệt
B Doug brought me here
i love your face. thanks
"Fusing sensors together reduces the combined noise by a factor of the square root of the number of sensors"
Why is that?
It comes from multiplying the probabilities of two distributions - or combining two Gaussians. Check out equation 11 here: www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/. If you have two noisy sensors, each with the same variance, sigma0^2 = sigma1^2, then when you blend them together the new variance is 1/2 of the individual sensor variances. Or, if you report noise as a standard deviation instead of variance then the combined standard deviations is 1/sqrt(2) of the individual sensors. Or the square root of 2 less. This works out with 3, 4, 5, or more sensors. As long as they have the same variance then the blended variance is 1/(number of sensors) and the blended standard deviation is 1/sqrt(number of sensors).
I'm curious about this too , and i realized that the creator of this clip is answer this by himself . Thank you for more information . your clips are very useful and have a very good explanation :)
Controlin its thinking ⚖️manuberin cuers
Create the word make a good covi like robat mechanic
On aure side
Ok make the faunten of uth joke
Just wonder how mistaken and biased it gets in a third world country.