@inproceedings{2d03d34c3cda41e387d6c6027a75c212,
title = "Efficient Traversability Mapping Based on Single Camera and 3D LiDAR",
abstract = "Mobile robots that are deployed both indoors and outdoors require the capability of recognizing their environment in real-time to improve their autonomous navigation. Many researchers study the leverage of cameras and light detection and ranging (LiDAR) sensors in combination to generate a representation of the environment. LiDAR is widely adopted for its ability to create high-precision maps in mobile robots and autonomous vehicles. With advanced deep learning techniques, various camera perception methods such as semantic segmentation, object detection, and classification represented remarkable performance. In this paper, we propose an efficient traversability map that fuses the recognition capability of cameras with the accurate mapping of 3D (LiDAR) sensors. Finally, we show an approximate F1 score performance of 0.83 compared to the LiDAR-based map generation method.",
keywords = "Mobile robots, Semantic map, Semantic segmentation, Semantic slam, Traversability map",
author = "Chanmin Youn and Wonkeun Youn and Sanghyun Kim and Jinseong Park and Shin, \{Young Sik\}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 18th International Conference on Intelligent Autonomous Systems, IAS18 2023 ; Conference date: 04-07-2023 Through 07-07-2023",
year = "2024",
doi = "10.1007/978-3-031-44851-5\_47",
language = "English",
isbn = "9783031448508",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "607--615",
editor = "Soon-Geul Lee and Jinung An and Chong, \{Nak Young\} and Marcus Strand and Kim, \{Joo H.\}",
booktitle = "Intelligent Autonomous Systems 18 - Volume 1 Proceedings of the 18th International Conference IAS18-2023",
address = "Germany",
}