VIZARD: Reliable Visual Localization for Autonomous Vehicles in Urban Outdoor Environments
| Autor: | Lukas Schaupp, Roland Siegwart, Mathias Bürki, Marcin Dymczyk, Cesar Cadena, Renaud Dubé, Juan Nieto |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
050210 logistics & transportation 0209 industrial biotechnology Recall Computer science business.industry 05 social sciences 02 engineering and technology Visual localization Computer Science - Robotics 020901 industrial engineering & automation Feature (computer vision) 11. Sustainability 0502 economics and business Key (cryptography) Computer vision Artificial intelligence Localization system business Robotics (cs.RO) |
| Zdroj: | 2019 IEEE Intelligent Vehicles Symposium (IV) |
| Popis: | Changes in appearance is one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present VIZARD, a visual localization system for urban outdoor environments. By combining a local localization algorithm with the use of multi-session maps, a high localization recall can be achieved across vastly different appearance conditions. The fusion of the visual localization constraints with wheel-odometry in a state estimation framework further guarantees smooth and accurate pose estimates. In an extensive experimental evaluation on several hundreds of driving kilometers in challenging urban outdoor environments, we analyze the recall and accuracy of our localization system, investigate its key parameters and boundary conditions, and compare different types of feature descriptors. Our results show that VIZARD is able to achieve nearly 100% recall with a localization accuracy below 0.5m under varying outdoor appearance conditions, including at night-time. Comment: 9 pages |
| Databáze: | OpenAIRE |
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