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Driving in unknown areas: From UAV images to map for autonomous vehicles

Published: 06 November 2018 Publication History

Abstract

Along with the rapid development of autonomous vehicles and driving assistance systems, suitable maps have been intensively studied in recent years. Besides the improvement of conventional maps, new types such as high-density (HD) maps, have been introduced to provide comprehensive detailed information particularly for autonomous driving. In the areas which are not or not well covered by these maps, however, the autonomous vehicles are basically on their own. I.e., besides GNSS signals, they have to rely solely on the onboard sensors with local measurements. In this paper, we present an alternative pipeline for map generation from UAV imagery. A map particularly suitable for autonomous driving is derived from the 3D scene reconstructed from high-resolution images. In addition to basic geometry and semantic features, the map contains abstract vertical landmarks for fast and accurate positioning and path planning, elevation as well as slope information for driving, and trafficability information for different types of vehicles in off-road areas. The potential of the proposed approach is demonstrated based on the results of an experiment.

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Cited By

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  • (2021)Mapping for Autonomous Driving: Opportunities and ChallengesIEEE Intelligent Transportation Systems Magazine10.1109/MITS.2020.301415213:1(91-106)Online publication date: Sep-2022

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    cover image ACM Conferences
    IWCTS'18: Proceedings of the 11th ACM SIGSPATIAL International Workshop on Computational Transportation Science
    November 2018
    75 pages
    ISBN:9781450360371
    DOI:10.1145/3283207
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 06 November 2018

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    Author Tags

    1. 3D reconstruction
    2. Autonomous driving
    3. Map construction
    4. OSM
    5. Scene analysis

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    IWCTS'18 Paper Acceptance Rate 8 of 11 submissions, 73%;
    Overall Acceptance Rate 42 of 57 submissions, 74%

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    • (2021)Mapping for Autonomous Driving: Opportunities and ChallengesIEEE Intelligent Transportation Systems Magazine10.1109/MITS.2020.301415213:1(91-106)Online publication date: Sep-2022

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