Understanding Sensor Fusion and Tracking, Part 1: What Is Sensor Fusion?

Check out the other videos in the series: Part 2 - Fusing an Accel, Mag, and Gyro to Estimation Orientation: https://www.seevid.ir/fa/w/0rlvvYgmTvI Part 3 - Fusing a GPS and IMU to Estimate Pose: https://www.seevid.ir/fa/w/hN8dL55rP5I Part 4 - Tracking a Single Object With an IMM Filter: https://www.seevid.ir/fa/w/hJG08iWlres Part 5 - How to Track Multiple Objects at Once: https://www.seevid.ir/fa/w/IIt1LHIHYc4 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: https://bit.ly/2pnEA6a -------------------------------------------------------------------------------------------------------- Get a free product trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See what's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2019 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.

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