Localization
Imagine a self-driving car navigating the roads or a robot serving your dining table. To understand where it is going, the robot must first answer a simple, yet fundamental question: "Where am I?" Without knowing its location, even the most advanced robots would wander aimlessly. It's like trying to pick up a spoon, but having no knowledge of where your hand is or how it needs to move, coordination becomes impossible. The solution is Localization.
In this section, we will explore four modules, each introducing key approaches in robot localization. Each module builds on the last, starting from basic information to three increasingly complex techniques:
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Preliminaries: Introductory terminology and technology used for localization.
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Dead-Reckoning: One of the simplest methods of localization, which estimates position by tracking the robot’s wheel rotations and movement over time. While easy to implement, it quickly accumulates error (drift).
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Bayes Filter: A probabilistic framework that updates the robot’s belief about its position by combining predictions from motion (like dead reckoning) with corrections from sensor measurements.
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Particle Filter: An advanced localization method that extends the Bayes Filter. It represents the robot’s possible positions as a set of “particles,” using sensor data and motion updates to refine the estimate. This approach is robust to noise and can handle complex, real-world environments.
Please note, that it is highly encouraged to move through these modules in order starting with the Preliminary section.
