| 제목 |
자율 주행을 위한 딥러닝 기반 라이다 객체 인식 신경망 연구 분석 |
| 분야 |
전기ㆍ전자ㆍ통신 |
| 언어 |
Korean |
| 저자 |
선민혁(한국과학기술원), 백동희(한국과학기술원), 공승현(한국과학기술원) |
| Key Words |
Autonomous driving(자율주행), Deep learning(딥러닝), Lidar(라이다), 3D Object detection(삼차원 객체 인식), Neural network(신경망) |
| 초록 |
Object detection is one of the most crucial functions for autonomous driving because path planning, obstacle avoidance, and numerous other functions rely on the acquired information regarding the positions of objects on the road. To enable accurate object detection, numerous works utilize lidar as the primary sensor since it can accurately acquire 3D measurements and it is robust to adverse environmental conditions such as poor illumination. In this work, we aim to comprehensively review deep learning-based object detection using lidar, which has shown remarkable detection performance on various datasets. First, we explain the general concepts of deep learning-based lidar object detection along with the datasets and benchmarks that are commonly used in existing works. We then thoroughly discuss the latest state-of-the-art neural networks for lidar object detection. Finally, we provide suggestions on how to employ these networks in an autonomous driving system. |
| 원문(PDF) |
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