Robotic Perception & Sensor Fusion: Enabling Machines to See and Understand
artificial-intelligence-ai.
Course Modules:
Module 1: Introduction to Robotic Perception
What is perception in robotics?
Active vs. passive sensors
Challenges in real-world perception: noise, uncertainty, and dynamics
Module 2: Sensor Types and Applications
Cameras (RGB, depth, stereo) and computer vision
LIDAR and 3D point clouds
Inertial Measurement Units (IMUs), GPS, and ultrasonic sensors
Module 3: Sensor Fusion Fundamentals
Why fuse sensors? Complementary vs. redundant data
Temporal alignment, synchronization, and calibration
Introduction to probabilistic fusion methods
Module 4: Fusion Techniques and Filters
Kalman Filter and Extended Kalman Filter (EKF)
Particle Filters for non-linear tracking
Examples in localization and tracking
Module 5: Perception for SLAM and Navigation
Simultaneous Localization and Mapping (SLAM)
Using fused data for obstacle avoidance and path planning
Real-world examples: autonomous vehicles, drones, service robots
Module 6: Capstone Project – Build a Perception Pipeline
Simulate or analyze multi-sensor data (e.g., camera + IMU + LIDAR)
Implement a basic fusion algorithm (e.g., EKF for localization)
Submit visualizations, data logs, and a report on system performance
Tools & Technologies Used:
Python
ROS (Robot Operating System)
OpenCV (vision), PCL (LIDAR), NumPy, Matplotlib
Gazebo, RViz, or Webots for simulation
Target Audience:
Robotics students and AI engineers
Developers working on autonomous systems
Learners interested in embedded sensing and control
Researchers in robotics, drones, and smart vehicles
Global Learning Benefits:
Equip robots with vision and spatial awareness
Master real-world sensor integration for stability and safety
Build perception pipelines for SLAM, mapping, and navigation
Gain practical experience with filtering and fusion algorithms
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