Obstacle Risk Assessment for Unmanned Surface Vehicle Using Camera and Lidar

This research aims to enhance vessel capabilities in assessing dynamically moving obstacles, such as floating debris and other vessels, by leveraging data from cameras and 3D Lidar sensors. YOLOv8 is trained to detect obstacles, with a specific focus o n s hip vessels. Point cloud processing is empl...

Full description

Saved in:

Bibliographic Details
Published in:	2024 21st International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) pp. 1 - 9
Main Authors:	Buasri, Jarunyawat, Thamrongaphichartkul, Kitti, Vongbunyong, Supachai, Charubhun, Weerawut, Buddhachan, Varunyou
Format:	Conference Proceeding
Language:	English
Published:	IEEE 27-05-2024
Subjects:	Automated surface vehicle Cameras Collision assess-ment Estimation Laser radar Lidar Object positioning in 3D Obstacle avoidance Point cloud compression Sensors State estimation Telecommunications Three-dimensional displays
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	This research aims to enhance vessel capabilities in assessing dynamically moving obstacles, such as floating debris and other vessels, by leveraging data from cameras and 3D Lidar sensors. YOLOv8 is trained to detect obstacles, with a specific focus o n s hip vessels. Point cloud processing is employed to identify clustered point clouds and match them with bounding boxes. The position obtained from the previous step is then utilized to estimate velocity using the Kalman filter. The system provides realtime information on the closest distance of approach and the remaining time before potential collisions occur. We use the remaining time before potential collisions occur to determine whether the obstacle is approaching or receding from us.
ISSN:	2837-6471
DOI:	10.1109/ECTI-CON60892.2024.10594884