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Chapter 3: Perception and Sensor Fusion

Perception forms the basis for intelligent robotic behavior, enabling robots to interpret and engage with their environments through data from various sensors. The chapter delves into multimodal sensing, 3D environmental understanding, and the integration of sensory data using probabilistic models to facilitate autonomous navigation and decision-making in dynamic settings.

Sections

  • 3

    Perception And Sensor Fusion

    This section explores how robots decode their environments through multimodal sensing and sensor fusion techniques.

    Learning Practice

  • 3.1

    Multimodal Sensing In Robotics

    Multimodal sensing involves utilizing various sensors to gather different types of data about an environment, essential for comprehensive robot perception.

    Learning Practice

  • 3.2

    3d Perception And Slam Techniques

    3D perception allows robots to accurately navigate and manipulate their environment, while SLAM techniques enable simultaneous mapping and localization.

    Learning Practice

  • 3.3

    Sensor Calibration And Noise Modeling

    This section discusses the importance of sensor calibration and noise modeling for accurate data interpretation in robotics.

    Learning Practice

  • 3.4

    Bayesian Sensor Fusion And Kalman Filters

    Bayesian sensor fusion combines multiple sensor inputs to enhance accuracy and reliability in uncertain environments, while the Kalman Filter estimates system states over time.

    Learning Practice

  • 3.5

    Real-Time Sensor Data Processing Pipelines

    Real-time sensor data processing pipelines enable robots to effectively interpret and respond to their environments by processing sensory information promptly.

    Learning Practice

References

Untitled document (19).pdf

Class Notes

Memorization

What we have learnt

  • Multimodal sensors (vision,...
  • SLAM techniques allow auton...
  • Calibration and noise model...

Final Test

Revision Tests

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