Leveraging Dynamic Occupancy Grids for 3D Object Detection in Point Clouds
| Autor: | Ozgur Erkent, Jilles S. Dibangoye, David Sierra-Gonzalez, Anshul Paigwar, Christian Laugier |
|---|---|
| Přispěvatelé: | Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Robots coopératifs et adaptés à la présence humaine en environnements dynamiques (CHROMA), 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), Université de Lyon-Institut National des Sciences Appliquées (INSA), Sierra-Gonzalez, David, Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon, Institut National des Sciences Appliquées (INSA)-Université de Lyon |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
Occupancy grid mapping Computer science business.industry Orientation (computer vision) [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO] 010401 analytical chemistry Feature extraction Point cloud 010501 environmental sciences Object (computer science) 01 natural sciences [STAT.ML] Statistics [stat]/Machine Learning [stat.ML] Object detection 0104 chemical sciences [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Vehicle dynamics [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] Minimum bounding box [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] Computer vision Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | ICARCV 2020-16th IEEE International Conference on Control, Automation, Robotics and Vision ICARCV 2020-16th IEEE International Conference on Control, Automation, Robotics and Vision, Dec 2020, Shenzhen, China. pp.1-6 ICARCV |
| Popis: | International audience; Traditionally, point cloud-based 3D object detectors are trained on annotated, non-sequential samples taken from driving sequences (e.g. the KITTI dataset). However, by doing this, the developed algorithms renounce to exploit any dynamic information from the driving sequences. It is reasonable to think that this information, which is available at test time when deploying the models in the experimental vehicles, could have significant predictive potential for the object detection task. To study the advantages that this kind of information could provide, we construct a dataset of dynamic occupancy grid maps from the raw KITTI dataset and find the correspondence to each of the KITTI 3D object detection dataset samples. By training a Lidar-based state-of-the-art 3D object detector with and without the dynamic information we get insights into the predictive value of the dynamics. Our results show that having access to the environment dynamics improves by 27% the ability of the detection algorithm to predict the orientation of smaller obstacles such as pedestrians. Furthermore, the 3D and bird's eye view bounding box predictions for pedestrians in challenging cases also see a 7% improvement. Qualitatively speaking, the dynamics help with the detection of partially occluded and far-away obstacles. We illustrate this fact with numerous qualitative prediction results. |
| Databáze: | OpenAIRE |
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