XDvision: Dense outdoor perception for autonomous vehicles
| Autor: | Victor Romero-Cano, Christian Laugier, Nicolas Vignard |
|---|---|
| Přispěvatelé: | Robots coopératifs et adaptés à la présence humaine en environnements dynamiques (CHROMA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), Toyota Motor Europe, Research work supported by Toyota Motor Europe, Toyota Motor Europe (BELGIUM) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Computer science media_common.quotation_subject Feature extraction 02 engineering and technology Image color analysis [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 020901 industrial engineering & automation Perception Robot sensing systems 0202 electrical engineering electronic engineering information engineering [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] Computer vision Image sensor Representation (mathematics) media_common Image segmentation business.industry Cameras Range (mathematics) Laser radar Lidar Robot 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | IEEE Intelligent Vehicle conference 2017 (IV 2017) IEEE Intelligent Vehicle conference 2017 (IV 2017), Jun 2017, Redondo Beach, Los Angeles, United States. ⟨10.1109/IVS.2017.7995807⟩ Intelligent Vehicles Symposium |
| DOI: | 10.1109/IVS.2017.7995807⟩ |
| Popis: | International audience; Robust perception is the cornerstone of safe and environmentally-aware autonomous navigation systems. Autonomous robots are expected to recognise the objects in their surroundings under a wide range of challenging environmental conditions. This problem has been tackled by combining multiple sensor modalities that have complementary characteristics. This paper proposes an approach to multi-sensor-based robotic perception that leverages the rich and dense appearance information provided by camera sensors, and the range data provided by active sensors independently of how dense their measurements are. We introduce a framework we call XDvision where colour images are augmented with dense depth information obtained from sparser sensors such as lidars. We demonstrate the utility of our framework by comparing the performance of a standard CNN-based image classifier fed with image data only with the performance of a two-layer multimodal CNN trained using our augmented representation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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