Autonomous Navigation without HD Prior Maps

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, predi...

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Bibliographic Details
Main Author: Ort, Teddy
Other Authors: Rus, Daniela
Format: Thesis
Published: Massachusetts Institute of Technology 2023
Online Access:https://hdl.handle.net/1721.1/147214
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author Ort, Teddy
author2 Rus, Daniela
author_facet Rus, Daniela
Ort, Teddy
author_sort Ort, Teddy
collection MIT
description 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.
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spelling mit-1721.1/1472142023-01-20T03:56:52Z Autonomous Navigation without HD Prior Maps Ort, Teddy Rus, Daniela Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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. Ph.D. 2023-01-19T18:36:50Z 2023-01-19T18:36:50Z 2022-09 2022-10-19T19:09:54.662Z Thesis https://hdl.handle.net/1721.1/147214 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology
spellingShingle Ort, Teddy
Autonomous Navigation without HD Prior Maps
title Autonomous Navigation without HD Prior Maps
title_full Autonomous Navigation without HD Prior Maps
title_fullStr Autonomous Navigation without HD Prior Maps
title_full_unstemmed Autonomous Navigation without HD Prior Maps
title_short Autonomous Navigation without HD Prior Maps
title_sort autonomous navigation without hd prior maps
url https://hdl.handle.net/1721.1/147214
work_keys_str_mv AT ortteddy autonomousnavigationwithouthdpriormaps