Robotics Advance | Chapter 3: Perception and Sensor Fusion by Prakhar Chauhan | Learn Smarter
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skillsβ€”perfect for learners of all ages.

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.

Enroll to start learning

You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.

Sections

  • 3

    Perception And Sensor Fusion

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

  • 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.

  • 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.

  • 3.3

    Sensor Calibration And Noise Modeling

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

  • 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.

  • 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.

Class Notes

Memorization

What we have learnt

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

Final Test

Revision Tests