Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System
| Autor: | Sixing Hu, Peidong Liu, Ye Chuan Yeo, Andreas Geiger, Benjamin Choi, Gim Hee Lee, Lionel Heng, Rang Nguyen, Benson Kuan, Torsten Sattler, Marc Pollefeys, Marcel Geppert, Zhaopeng Cui |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer science business.industry Deep learning media_common.quotation_subject Real-time computing 02 engineering and technology Multi camera Computer Science - Robotics 020901 industrial engineering & automation Perception 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Robotics (cs.RO) Envelope (motion) media_common |
| Zdroj: | ICRA |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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