Learn how you can use MATLAB® and Simulink® to design perception systems for robots and unmanned vehicles. MathWorks experts share their knowledge of topics such as computer vision, deep learning, and signal processing with a focus on helping robots and unmanned systems understand what’s in its surroundings. This video series will also feature student teams that have successfully used MATLAB and Simulink for perception.
Obstacle Avoidance Using a Camera Sensor Learn how to autonomously navigate your vehicle through obstacles with the help of a front-facing camera using an optical flow algorithm.
Labeling Ground Truth for Object Detection Use the Ground Truth Labeler app to generate quality ground truth data that can be used to train and evaluate object detectors.
Training and Validating Object Detectors Use labeled ground truth data to train and evaluate object detectors.
Sensor Fusion for Orientation Estimation Join Roberto Valenti and Connell D’Souza as they discuss using Sensor Fusion and Tracking Toolbox to perform sensor fusion for orientation estimation.
Designing Digital Filters with MATLAB Join Mark Schwab and Connell D'Souza as they demonstrate the use of the Filter Designer app and interactively design filters for digital signal processing that can be implemented in MATLAB or Simulink.
Estimating Direction of Arrival with MATLAB Stephen Cronin from the Robotics Association at Embry-Riddle Aeronautical University demonstrates how to detect the direction of arrival of an underwater acoustic signal using MATLAB.
Data Preprocessing for Deep Learning Learn how to resize images, create labeled training, validation, and test datasets to train and test object detection models, as Neha Goel joins Connell D’Souza to talk about data preprocessing for deep learning.
Design and Train a YOLOv2 Network in MATLAB Neha Goel joins Connell D’Souza to talk about designing and training a YOLOv2 real-time object detection neural network.
Import Pretrained Deep Learning Networks into MATLAB Neha Goel joins Connell D’Souza to demonstrate how to use Open Neural Network Exchange (ONNX) to import pretrained deep learning networks into MATLAB and perform transfer learning.