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Computer Vision & Robotics Course

Computer Vision & Robotics Course
4 to 360 hours of flexible workload
certificate valid in your country

What Will I Learn?

Gain hands-on expertise in developing dependable mobile robots for warehouse environments. Master geometric vision principles, camera calibration, image formation techniques, and reliable feature extraction methods. Progress to advanced topics like contemporary object detection, tracking, segmentation, and depth estimation. Additionally, acquire skills in dataset creation, synthetic data generation, deployment on embedded GPUs, safety-oriented human detection, occupancy mapping, and integrated navigation systems.

Elevify Advantages

Develop Skills

  • Vision geometry and calibration: develop precise camera models for mobile robots.
  • Real-time detection: implement efficient YOLO-based models on embedded GPUs securely.
  • Human tracking for safety: identify, follow, and assess people near robots.
  • Free-space and obstacle mapping: integrate RGB, depth, and lidar data into occupancy grids.
  • Dataset and domain adaptation: build, annotate, and tailor warehouse vision datasets.

Suggested Summary

Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.
Workload: between 4 and 360 hours

What our students say

Your lessons are perfect. I purchased the one-year package and finally have the opportunity to follow various topics of my interest without needing to change platforms... I thank you for everything you do, I've already recommended you to other people...
Giulio Carlo
Giulio CarloDigital Marketing Student
I like how the lessons are straight to the point and how I can switch chapters and skip content I don't need.
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Mariana FerresPhotography Student
I like the content and the way videos are presented and transcribed, which speeds up the process!
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Luciana AlvarengaNail Design Student
The platform is fast, simple to use. The diversity of content and complementary videos really help with learning.
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André FelipePrompt Engineering Student

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