A brief understanding of Sensor Fusion and Tracking with examples along with Advantages & Algorithm

Mocap Suit Building Part 9
In this video, I have tried to explain what is sensors and what are the sensors are used for tracking. How multiple sensors are fused together to get an aggregated outcome about orientation and heading reference. I also have touch upon autonomous systems and please process follows to make decision. How such technological output are mathematically used to derive approximation. I have tried to provide some examples of the sensor fusion to lowering the uncertainty, increase the reliability, to improve the trustworthiness. How sensors are used to predict the future and how the prediction are used in estimation. Finally, I touched upon some algorithms basics and what next I will use for my motion capture suit.

This post detailed writeup:   / 62675791  
Information and Links:
Accelerometer:    • Accelerometer Explained - simplified ...  
Gyroscope:    • Basic principles behind Gyroscope | H...   (Part1) and    • Gyroscope | Rigidity in Space and Pre...   (Part2)

Previous Paper Post:   / 62369320  
Playlist:    • Building motion capture / mocap suit ...  

Patreon:   / themakingofothers  

Special Thanks to:
Matlab and Brian Douglas
Part 1 - What Is Sensor Fusion?:    • Understanding Sensor Fusion and Track...  
Part 2 - Fusing an Accel, Mag, and Gyro to Estimation Orientation:    • Understanding Sensor Fusion and Track...  
Bosch Mobility Solutions -    / @boschmobility  

Video Chapters:
00:00 What is Sensors with examples
00:39 Inertial measurement units (IMU) Sensors / how is it used
02:19 Autonomous system definition with examples
02:51 What is Sensor Fusion / why sensor fusion is required
03:28 Different Stages of Sensor Fusion / Process of Sensor Fusion / Process of making autonomous system
05:11 Different types of activities performed by a sensor
06:02 Advantages of Sensor Fusion with examples
08:01 How Sensor Fusion is used to lowering the uncertainty
09:02 How sensor fusion is used to increase the reliability
11:04 Sensor fusion for prediction
12:!3 Sensor fusion is to increase the coverage area of prediction model
13:11 Attitude and Heading Reference System (AHRS)
14:38 Dead reckoning – Gyroscope process
15:11 Downside of dead reckoning process
16:11 Sensor fusion algorithm basics
16:49 Complementary Filter, Kalman Filter, Mahony Filter and Madgwick Filter algorithm high level
18:41 Conclusion

Music source: https://mixkit.co/free-sound-effects
Other video sources: https://www.pexels.com/ and others from youtube like BMW, Mazda and Others.

I am not an expert, I am learning while I am making this video. If I am making mistakes please help and please comment your opinion
Thank you for watching

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