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(2) In future presentations of the research findings, in addition to the course project website and public presentations, your real name and personal information will not appear in this research report. If you are interested in the research results, we can provide you with an executive summary after the study is completed.
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Uedu Open / Visual Navigation for Autonomous Vehicles (VNAV)
16.485

Visual Navigation for Autonomous Vehicles (VNAV)

Prof. Luca Carlone, Kasra Khosoussi, Markus Ryll, Golnaz Habibi, Vasileios Tzuomas, Rajat Talak | Fall 2020
Data Science, Analytics & Computer Technology AI Machine Learning Visualization Computer Science Engineering Aerospace Engineering Artificial Intelligence
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CC BY-NC-SA 4.0
課程簡介
This course covers the mathematical foundations and state-of-the-art implementations of algorithms for vision-based navigation of autonomous vehicles (e.g., mobile robots, self-driving cars, drones). It provides students with a rigorous but pragmatic overview of differential geometry and optimization on manifolds and knowledge of the fundamentals of 2-view and multi-view geometric vision for real-time motion estimation, calibration, localization, and mapping. The theoretical foundations are complemented with hands-on labs based on state-of-the-art mini racecar and drone platforms. It culminates in a critical review of recent advances in the field and a team project aimed at advancing the state of the art.
Course Information
SourceMIT 開放式課程
科系Aeronautics and Astronautics
LanguageEnglish
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