THESIS DEFENSE: Autonomous Navigation without HD Prior Maps


Teddy Ort


Daniela Rus
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Abstract: Most fielded autonomous driving systems currently rely on High Definition (HD) prior maps both to localize, and to retrieve detailed geometric and semantic information about the environment. This information is necessary to enable safe operation of many downstream driving components including, prediction, planning, and control. However, this requirement has raised issues with scalability, confining autonomous systems to small test regions where such detailed maps can be maintained. Furthermore, the reliance on HD maps can prevent autonomous vehicles from realizing human-like flexibility to both explore new areas and successfully navigate in rapidly changing environments or weather conditions. In this thesis, we present MapLite, an autonomous navigation system using only Standard Definition (SD) prior maps, in conjunction with onboard perception to directly infer the necessary HD map online. We also explore the use of a Localizing Ground Penetrating Radar (LGPR) for precise localization using stable underground features that are robust to changing weather conditions. Together, these methods can reduce the requirement for HD prior maps and bring autonomous navigation closer to human levels of flexibility and robustness.

Bio: Teddy Ort is a final year PhD candidate at the Computer Science and Artificial Intelligence Laboratory at MIT advised by Prof. Daniela Rus. His research focuses on localization and navigation for autonomous vehicles in challenging environments where typical HD map based solutions can fail. He is interested in algorithms that extend the operating space of autonomous vehicles toward human level flexibility in dynamic environments. Prior to joining CSAIL, Teddy received his Bachelor’s Degree from the Department of Mechanical Engineering at MIT in 2016. In the Fall of 2022, he will join Symbotic, focusing on perception and AI for robots in warehousing and logistics.