自主车辆视觉导航:在麻省理工学院开设的开放源实际操作机器人课程 (Visual Navigation for Autonomous Vehicles: An Open-source Hands-on Robotics Course at MIT)

This paper reports on the development, execution, and open-sourcing of a new robotics course at MIT. The course is a modern take on "Visual Navigation for Autonomous Vehicles" (VNAV) and targets first-year graduate students and senior undergraduates with prior exposure to robotics. VNAV has the goal of preparing the students to perform research in robotics and vision-based navigation, with emphasis on drones and self-driving cars. The course spans the entire autonomous navigation pipeline; as such, it covers a broad set of topics, including geometric control and trajectory optimization, 2D and 3D computer vision, visual and visual-inertial odometry, place recognition, simultaneous localization and mapping, and geometric deep learning for perception. VNAV has three key features. First, it bridges traditional computer vision and robotics courses by exposing the challenges that are specific to embodied intelligence, e.g., limited computation and need for just-in-time and robust perception to close the loop over control and decision making. Second, it strikes a balance between depth and breadth by combining rigorous technical notes (including topics that are less explored in typical robotics courses, e.g., on-manifold optimization) with slides and videos showcasing the latest research results. Third, it provides a compelling approach to hands-on robotics education by leveraging a physical drone platform (mostly suitable for small residential courses) and a photo-realistic Unity-based simulator (open-source and scalable to large online courses). VNAV has been offered at MIT in the Falls of 2018-2021 and is now publicly available on MIT OpenCourseWare (OCW).

翻译：本文报告了麻省理工学院新机器人课程的开发、执行和开放外包情况。该课程是一个现代的“自主车辆视觉导航”课程(VNAV),针对一年级研究生和以前接触机器人的高级本科生。VNAV的目标是让学生准备对机器人和视觉导航进行研究,重点是无人驾驶飞机和自驾驶汽车。该课程涵盖整个自主导航管道;因此,该课程涵盖一系列广泛的主题,包括几何控制和轨迹优化、2D和3D计算机视野、视觉和视觉-内层测量、地点识别、同步本地化和绘图以及深深地测量认知。VNAV有三个关键特征。第一,它将传统的计算机视野和机器人课程连接起来,通过展示体现智能智能智能的智能,例如有限计算以及及时、稳健健的认知,以结束基于控制和决策的循环。第二,它通过将严格的技术说明(包括当前在典型的离层流系统课程中进行探索的大型滚动和绘图课程)在深度和广度之间保持平衡。在Sildroal-roal-roal-hole 上,它提供了一种最接近的硬的硬的图像课程。在Siral-roal-hoal-hoal-roal-hoal-le-hole-to-to-to-to-to-to-to-to-to-lex-to-tolex-toal-toal-lex-toal-toal-toal-toal-to-lex-to-lex-lex-to-to-to-to-to-to-toal-todal-tod-to-to-to-tod-tod-to-to-to-to-to-to-to-tod-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-tod-le-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-to-tod-tod-tod-to-to-to-tod-to-to-