ETLi: Efficiently annotated traffic LiDAR dataset using incremental and suggestive annotation

• Main Authors: Jungyu Kang, Seung‐Jun Han, Nahyeon Kim, Kyoung‐Wook Min
• Format: Article
• Language: English
• Published: Electronics and Telecommunications Research Institute (ETRI) 2021-09-01
• Series: ETRI Journal
• Subjects: autonomous driving; deep‐learning dataset; light detection and ranging; semantic segmentation; semantic simultaneous localization and mapping
• Online Access: https://doi.org/10.4218/etrij.2021-0055

Autonomous driving requires a computerized perception of the environment for safety and machine‐learning evaluation. Recognizing semantic information is difficult, as the objective is to instantly recognize and distinguish items in the environment. Training a model with real‐time semantic capability and high reliability requires extensive and specialized datasets. However, generalized datasets are unavailable and are typically difficult to construct for specific tasks. Hence, a light detection and ranging semantic dataset suitable for semantic simultaneous localization and mapping and specialized for autonomous driving is proposed. This dataset is provided in a form that can be easily used by users familiar with existing two‐dimensional image datasets, and it contains various weather and light conditions collected from a complex and diverse practical setting. An incremental and suggestive annotation routine is proposed to improve annotation efficiency. A model is trained to simultaneously predict segmentation labels and suggest class‐representative frames. Experimental results demonstrate that the proposed algorithm yields a more efficient dataset than uniformly sampled datasets.