A Deep Learning Approach for LiDAR Resolution-Agnostic Object Detection
| Autor: | Ruddy Theodose, Dieumet Denis, Thierry Chateau, Vincent Fremont, Paul Checchin |
|---|---|
| Přispěvatelé: | Université Clermont Auvergne (UCA), Institut Pascal (IP), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Sherpa Engineering, École Centrale de Nantes (ECN), Laboratoire des Sciences du Numérique de Nantes (LS2N), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Autonomie des Robots et Maîtrise des interactions avec l’ENvironnement (ARMEN), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
050210 logistics & transportation
Sensor Fusion Mechanical Engineering Camera Sensor 05 social sciences LiDAR Sensor [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Intelligent Vehicle Computer Science Applications [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Deep Learning 0502 economics and business Automotive Engineering 3D Object Detection [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] ComputingMilieux_MISCELLANEOUS |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems IEEE Transactions on Intelligent Transportation Systems, IEEE, 2021, pp.1-12. ⟨10.1109/TITS.2021.3130487⟩ IEEE Transactions on Intelligent Transportation Systems, 2021, pp.1-12. ⟨10.1109/TITS.2021.3130487⟩ |
| ISSN: | 1524-9050 |
| DOI: | 10.1109/TITS.2021.3130487⟩ |
| Popis: | International audience; Existing neural network-based object detection approaches process LiDAR point clouds trained from one kind of LiDAR sensor. In the case of a different point cloud input, the trained network performs with less efficiency, especially when the given point cloud has low resolution. In this paper, we propose a new object detection approach, which is more resilient to variations in point cloud resolution. Firstly, layers from the point cloud are randomly discarded during the training phase in order to increase the variability of the data processed by the network. Secondly, the obstacles are described as Gaussian functions, grouping multiple parameters into a single representation. A Bhattacharyya distance is used as a loss function. This approach is tested on a LiDAR-based network and on an architecture using camera and LiDAR sensors. The networks are trained exclusively on the KITTI dataset and tested on Pandaset and the nuScenes Mini dataset. Experiments show that our method improves the performance of the tested networks on low-resolution point clouds without decreasing the ability to process high-resolution data. |
| Databáze: | OpenAIRE |
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