@inproceedings{e431e8498bef42eaa3a38217ff737075,
title = "Non-Calibration Sensor Fusion Using High Resolution 3D LiDAR",
abstract = "Light detection and ranging (LiDAR) and camera are the most commonly used combination in sensor fusion techniques. In this paper, we propose a method to recognize obstacles by fusion of images and point cloud data generated from high-resolution 3D LiDAR. By applying the image to a deep learning network, we obtain inference information about the obstacle, and based on this, we convert the pixel location of the obstacle in the image into a mapped point cloud. This enables more sophisticated obstacle recognition by converting 2D data into 3D space data.",
keywords = "Autonomous Driving, LiDAR, Object Detection, Sensor Fusion",
author = "Eun, \{Seung Woo\} and Bitwire, \{George Albert\} and Thu, \{Nguyen Thi Hoai\} and Kang, \{Sin Jae\} and Han, \{Dong Seog\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 15th International Conference on Information and Communication Technology Convergence, ICTC 2024 ; Conference date: 16-10-2024 Through 18-10-2024",
year = "2024",
doi = "10.1109/ICTC62082.2024.10827034",
language = "English",
series = "International Conference on ICT Convergence",
publisher = "IEEE Computer Society",
pages = "158--159",
booktitle = "ICTC 2024 - 15th International Conference on ICT Convergence",
address = "United States",
}