Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System
2019
Conference Paper
avg
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(s): | Lionel Heng and Benjamin Choi and Zhaopeng Cui and Marcel Geppert and Sixing Hu and Benson Kuan and Peidong Liu and Rang M. H. Nguyen and Ye Chuan Yeo and Andreas Geiger and Gim Hee Lee and Marc Pollefeys and Torsten Sattler |
| Book Title: | Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2019 |
| Year: | 2019 |
| Month: | May |
| Publisher: | IEEE |
| Department(s): | Autonomous Vision |
| Bibtex Type: | Conference Paper (inproceedings) |
| Event Name: | International Conference on Robotics and Automation |
| Event Place: | Montreal, Canada |
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BibTex @inproceedings{Heng2019ICRA,
title = {Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System},
author = {},
booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2019},
publisher = {IEEE},
month = may,
year = {2019},
doi = {},
month_numeric = {5}
}
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