Making Vehicles & Robots See-Getting Started with Perception

バージョン 1.0.8 (42.1 MB) 作成者: MathWorks Student Competitions Team
This repository is to help students to get started with practical approaches to work with perception algorithms using MATLAB and Simulink.
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Making Vehicles and Robots See: Getting Started with Perception for Students

This submission contains all of the example and exercise materials for the online tutorial series - Making Vehicles and Robots See: Getting Started with Perception for Students. Link to the video series: https://www.youtube.com/playlist?list=PLn8PRpmsu08pMH5wexyjc_4ludkyYvhqo.

While designing autonomous systems, there is typically a requirement to 'see' the environment. It could be an underwater vehicle navigating through obstacles, or a formula student car maneuvering through cones, or robot picking and placing an object.

This tutorial series will help you get started with the practical approaches while working with perception algorithms and how to tackle them. We will use MATLAB® as the platform to understand the algorithms and apply the techniques learnt using Simulink® through the exercises.

About the Folders

  1. 1_basic_operations_on_images:
    • This folder contains files to work with the basic operations such as reading, displaying, resizing, cropping, rotating, filtering and enhancement on images in MATLAB.
    • It concludes with an exercise to perform the learnt basic operations on images in Simulink.

  1. 2_image_segmentation_and_analysis
    • This folder contains files to segment an image based on colors, refine these detections, and analyze the resulting image regions all using interactive apps available in MATLAB.
    • It concludes with an exercise to perform color-based segmentation and blob detection using Simulink.

  1. 3_feature_matching_and_tracking
    • This folder contains files to detect, describe and match features between two images (demo1), and then to track an object in a video based on image features (demo2) using MATLAB.
    • It concludes with an exercise to perform video recovery using feature matching in Simulink.

  1. 4_basics_on_point_cloud_processing
    • This folder contains files for introducing the basics of point cloud processing by showing how to estimate the locations of objects in a 3D space using point clouds in MATLAB.
    • It concludes with an exercise to perform location estimation of poles using a point cloud generated by an aerial vehicle.

  1. 5_image-classification-with-deep-learning
    • This folder contains files to train and test a deep neural network to classify a dataset of images
    • It concludes with an exercise to perform image classification using the trained network in Simulink

About the Files

The main folder is divided as Parts specficified above. Each folder has files the following structure:

  • code: Contains MATLAB Live Scripts with all the commands used during the tutorial.
    • Open the MATLAB Live Script (.MLX file)
    • Step through each section of the code
    • Interact with the controls to see the result of the corresponsing operations.
  • exercise: Recommended exercises to apply the concepts learnt from the video.
    • Open the folder and the Simulink model
    • Fill the blank areas in the Simulink model according to the problem statement

Recommendations

Product Requirements

This repository uses the following MathWorks products:

  1. MATLAB
  2. Simulink
  3. Image Processing Toolbox ™
  4. Computer Vision Toolbox ™
  5. Lidar Toolbox ™
  6. Deep Learning Toolbox ™

In case of any questions, please reach out to us at roboticsarena@mathworks.com.

Copyright 2021-2023 The MathWorks, Inc.

引用

MathWorks Student Competitions Team (2024). Making Vehicles & Robots See-Getting Started with Perception (https://github.com/mathworks/getting-started-with-perception-for-students/releases/tag/1.0.8), GitHub. 取得済み .

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3_feature-matching-and-tracking/exercise

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2_image-segmentation-and-analysis/exercise

3_feature-matching-and-tracking/exercise

1_basic-operations-on-images/code

2_image-segmentation-and-analysis/code

3_feature-matching-and-tracking/code

4_basics-on-point-cloud-processing/code

4_basics-on-point-cloud-processing/exercise

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