OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios
| Autor: | Mathias Bürki, Cesar Cadena, Roland Siegwart, Lukas Schaupp, Renaud Dubé |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Range Sensing
FOS: Computer and information sciences 0209 industrial biotechnology business.industry Computer science 010401 analytical chemistry Point cloud ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Deep Learning in Robotics and Automation Pattern recognition 02 engineering and technology Function (mathematics) 01 natural sciences Convolutional neural network 0104 chemical sciences Localization Computer Science - Robotics 020901 industrial engineering & automation Lidar Artificial intelligence business Robotics (cs.RO) |
| Zdroj: | 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| ISSN: | 7281-4004 |
| Popis: | We introduce a novel method for oriented place recognition with 3D LiDAR scans. A Convolutional Neural Network is trained to extract compact descriptors from single 3D LiDAR scans. These can be used both to retrieve near-by place candidates from a map, and to estimate the yaw discrepancy needed for bootstrapping local registration methods. We employ a triplet loss function for training and use a hard-negative mining strategy to further increase the performance of our descriptor extractor. In an extensive evaluation on the NCLT and KITTI datasets, we demonstrate that our method outperforms related state-of-the-art approaches based on both data-driven and handcrafted data representation in challenging long-term outdoor conditions. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ISBN:978-1-7281-4004-9 ISBN:978-1-7281-4003-2 |
| Databáze: | OpenAIRE |
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