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

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.
Try Sensor Fusion and Tracking Toolbox: https://bit.ly/2VIcKha Learn more about Sensor Fusion and Tracking Toolbox: https://bit.ly/32isIB7 Explore sensor fusion examples: https://bit.ly/2qaMBvH Download white paper: Sensor Fusion and Tracking for Autonomous System: https://bit.ly/35CGWyI



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

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