Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System
Paper in proceedings, 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.

Author

Lionel Heng

DSO National Laboratories

Benjamin Choi

DSO National Laboratories

Zhaopeng Cui

Swiss Federal Institute of Technology in Zürich (ETH)

Marcel Geppert

Swiss Federal Institute of Technology in Zürich (ETH)

Sixing Hu

National University of Singapore (NUS)

Benson Kuan

DSO National Laboratories

Peidong Liu

Swiss Federal Institute of Technology in Zürich (ETH)

Rang Nguyen

National University of Singapore (NUS)

Ye Chuan Yeo

DSO National Laboratories

Andreas Geiger

Max Planck Society

University of Tübingen

Gim Hee Lee

National University of Singapore (NUS)

Marc Pollefeys

Microsoft

Swiss Federal Institute of Technology in Zürich (ETH)

Torsten Sattler

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)

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

4695-4702

Subject Categories

Embedded Systems

Robotics

Computer Vision and Robotics (Autonomous Systems)

More information

Latest update

1/24/2020