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- Robot Cognition and Learning
- Computer Vision for Robotics and Autonomous Systems
Curriculum
- 8 Sections
- 70 Lessons
- Lifetime
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- 1. Computer Vision Fundamentals9
- 1.13DJ8 1.1 Image Formation and Camera Geometry Basics
- 1.23DJ8 1.2 Pinhole Camera Model and Intrinsic Parameters
- 1.33DJ8 1.3 Color Spaces RGB, HSV, YCbCr for Robotics
- 1.43DJ8 1.4 Image Filtering, Kernels, and Convolution Operations
- 1.53DJ8 1.5 Edge Detection – Sobel and Canny Algorithms
- 1.63DJ8 1.6 Feature Detection – Harris, ORB, and SIFT
- 1.73DJ8 1.7 Introduction to Deep Learning for Vision
- 1.83DJ8 1.8 Convolutional Neural Networks – Architecture and Operations
- 1.93DJ8 1.9 OpenCV and PyTorch Fundamentals
- 2. Object Detection for Robotics9
- 2.13DJ8 2.1 Object Detection Task Formulation and Metrics
- 2.23DJ8 2.2 Classical Methods – Haar Cascades and HOG
- 2.33DJ8 2.3 YOLO Architecture and Real-Time Detection
- 2.43DJ8 2.4 Faster R-CNN and Two-Stage Detectors
- 2.53DJ8 2.5 SSD MobileNet for Efficient Detection
- 2.63DJ8 2.6 Training Custom Object Detectors
- 2.73DJ8 2.7 Real-Time Inference Optimization
- 2.83DJ8 2.8 Multi-Scale Detection and Pyramid Networks
- 2.93DJ8 2.9 Handling Small and Occluded Objects
- 3. Semantic and Instance Segmentation8
- 3.13DJ8 3.1 Semantic Segmentation – FCN and SegNet Architectures
- 3.23DJ8 3.2 Instance Segmentation with Mask R-CNN
- 3.33DJ8 3.3 Panoptic Segmentation – A Unified Approach
- 3.43DJ8 3.4 Encoder–Decoder Architectures – U-Net
- 3.53DJ8 3.5 Real-Time Segmentation for Robotics
- 3.63DJ8 3.6 Segmentation Loss Functions and Training
- 3.73DJ8 3.7 Post-Processing and Refinement Techniques
- 3.83DJ8 3.8 Handling Dense Prediction at Scale
- 4. Pose Estimation and 6D Localization9
- 4.13DJ8 4.1 Human Pose Estimation for Robotics
- 4.23DJ8 4.2 Keypoint Detection Networks
- 4.33DJ8 4.3 Object Pose Estimation from Images
- 4.43DJ8 4.4 6DoF Pose Regression and Classification
- 4.53DJ8 4.5 Leveraging Synthetic Data for Pose
- 4.63DJ8 4.6 Bundle Adjustment and Pose Refinement
- 4.73DJ8 4.7 Multi-Instance and Articulated Pose
- 4.83DJ8 4.8 Real-Time Pose Estimation Pipelines
- 4.93DJ8 4.9 Pose Uncertainty and Confidence Estimation
- 5. 3D Vision and Depth Perception9
- 5.13DJ8 5.1 Stereo Vision – Depth from Two Cameras
- 5.23DJ8 5.2 Epipolar Geometry and Camera Calibration
- 5.33DJ8 5.3 Depth Estimation – Monocular and Stereo Methods
- 5.43DJ8 5.4 Point Clouds and 3D Reconstruction
- 5.53DJ8 5.5 Structure from Motion (SfM) Techniques
- 5.63DJ8 5.6 LiDAR Integration and Point Cloud Processing
- 5.73DJ8 5.7 Sensor Fusion – Camera and LiDAR
- 5.83DJ8 5.8 Obstacle Detection from Depth
- 5.93DJ8 5.9 3D Object Detection and Localization
- 6. Object Tracking and Video Analysis8
- 6.13DJ8 6.1 Video Object Tracking and Temporal Modeling
- 6.23DJ8 6.2 Kalman Filters for Tracking
- 6.33DJ8 6.3 Optical Flow and Motion Estimation
- 6.43DJ8 6.4 Tracking Algorithms – KCF, SORT, DeepSORT
- 6.53DJ8 6.5 Multi-Object Tracking (MOT) Systems
- 6.63DJ8 6.6 Trajectory Prediction for Robotics
- 6.73DJ8 6.7 Re-Identification in Crowded Scenes
- 6.83DJ8 6.8 Video Understanding for Autonomous Robots
- 7. Edge Optimization and Deployment8
- 7.13DJ8 7.1 Model Compression – Quantization and Pruning
- 7.23DJ8 7.2 Knowledge Distillation for Edge Models
- 7.33DJ8 7.3 Neural Architecture Search under Constraints
- 7.43DJ8 7.4 Optimization for ARM Embedded Processors
- 7.53DJ8 7.5 TensorFlow Lite and ONNX Runtime
- 7.63DJ8 7.6 Deployment on NVIDIA Jetson (Nano to AGX)
- 7.73DJ8 7.7 Inference Latency – Profiling and Optimization
- 7.83DJ8 7.8 Bandwidth and Memory Constraints
- 8. Robotic Integration and Applications10
- 8.13DJ8 8.1 Visual Servoing and Closed-Loop Control
- 8.23DJ8 8.2 Pick-and-Place Manipulation with Vision
- 8.33DJ8 8.3 Navigation and Obstacle Avoidance
- 8.43DJ8 8.4 SLAM – Visual Localization and Mapping
- 8.53DJ8 8.5 Autonomous Vehicle Perception Stacks
- 8.63DJ8 8.6 Human–Robot Interaction – Visual Understanding
- 8.73DJ8 8.7 Failure Detection and Recovery Mechanisms
- 8.83DJ8 8.8 End-to-End Integration Testing
- 8.93DJ8 8.9 Real-World Deployment Challenges
- 8.103DJ8 8.10 Benchmarking Robotic Vision Systems
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