Sensor Models
Camera models, calibration techniques, and probabilistic sensor models including beam and likelihood field approaches.
CNNs
Convolutional neural networks for image classification, including layer types, architectures, and visualization techniques.
Object Detection
Detecting and localizing objects in images using deep learning approaches.
Object Segmentation
Pixel-wise classification for semantic and instance segmentation.
State Estimation
Recursive state estimation, Kalman filters, particle filters, and SLAM for robot localization and mapping.
Sensor Models
Sensor models provide the mathematical foundation for understanding how robots perceive their environment through cameras, lidar, and other sensors.Camera Models
Camera fundamentals and image processing for robotics.
Pinhole Model
Mathematical representation of the pinhole camera model.
Camera Calibration
Practical camera calibration using OpenCV.
Beam Models
Probabilistic models for range sensors.
Convolutional Neural Networks
CNNs are the backbone of modern computer vision systems, enabling image classification, feature extraction, and visual understanding.CNN Introduction
Introduction to convolutional neural networks.
CNN Layers
Understanding CNN layer types and operations.
CNN Architectures
Example architectures: LeNet, AlexNet, VGG, ResNet.
State Estimation
State estimation enables robots to determine their position and build maps of their environment using sensor observations.Recursive State Estimation
Foundations of recursive Bayesian estimation.
Kalman Filters
Linear Gaussian state estimation.
Occupancy Mapping
Building occupancy grid maps from sensor data.
SLAM
Simultaneous Localization and Mapping.