Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System
Paper i proceeding, 2019

Project AutoVision aims to develop localization and 3D scene perception capabilities for a self-driving vehicle. Such capabilities will enable autonomous navigation in urban and rural environments, in day and night, and with cameras as the only exteroceptive sensors. The sensor suite employs many cameras for both 360-degree coverage and accurate multi-view stereo; the use of low-cost cameras keeps the cost of this sensor suite to a minimum. In addition, the project seeks to extend the operating envelope to include GNSS-less conditions which are typical for environments with tall buildings, foliage, and tunnels. Emphasis is placed on leveraging multi-view geometry and deep learning to enable the vehicle to localize and perceive in 3D space. This paper presents an overview of the project, and describes the sensor suite and current progress in the areas of calibration, localization, and perception.

Författare

Lionel Heng

DSO National Laboratories

Benjamin Choi

DSO National Laboratories

Zhaopeng Cui

Eidgenössische Technische Hochschule Zürich (ETH)

Marcel Geppert

Eidgenössische Technische Hochschule Zürich (ETH)

Sixing Hu

Universiti Kebangsaan Singapura (NUS)

Benson Kuan

DSO National Laboratories

Peidong Liu

Eidgenössische Technische Hochschule Zürich (ETH)

Rang Nguyen

Universiti Kebangsaan Singapura (NUS)

Ye Chuan Yeo

DSO National Laboratories

Andreas Geiger

Max-Planck-Gesellschaft

Universität Tübingen

Gim Hee Lee

Universiti Kebangsaan Singapura (NUS)

Marc Pollefeys

Microsoft

Eidgenössische Technische Hochschule Zürich (ETH)

Torsten Sattler

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)

1050-4729 (ISSN) 2577-087X (eISSN)

4695-4702

Ämneskategorier

Inbäddad systemteknik

Robotteknik och automation

Datorseende och robotik (autonoma system)

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Senast uppdaterat

2020-01-24