Document Type

Conference Paper

Publication Date

2023

DOI

10.46354/i3m.2023.hms.007

Publication Title

Proceedings of the 25th International Conference on Harbor, Maritime and Multimodal Logistic Modeling & Simulation (HMS 2023)

Pages

007

Abstract

Safe operation of uncrewed maritime systems is a major concern in the presence of other vehicles or obstacles. Typically, perception algorithms utilize sensor data to identify obstacles that must be avoided, and AI algorithms are used to interpret raw sensor data for use in navigation and object avoidance algorithms. However, perception algorithms are typically computationally expensive. In this paper, we present an efficient method for detecting obstacles using raw lidar data in the form of range or Point Cloud, employing computationally efficient techniques that do not depend on trained models or AI matching. The approach
converts the sensor readings into the robot's local coordinate system, projecting it onto an occupancy map, and applying efficient image processing techniques to detect obstacles. As a rapid and easy to implement algorithm, the proposed work provides a practical solution for lidar-based maritime perception applications. This paper further focuses on detection of near-by objects with simple shapes, such as buoys or totems, which are commonly used in near-shore and near-harbor maritime environments. With the ability to detect obstacles efficiently, our algorithm can help ensure safe navigation when maneuvering these environments. Results show that the algorithm can accurately detect buoys and totems with minimal false positives.

Rights

© 2023 The Authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-Noncommercial-Noderivatives 4.0 International (CC BY-NC-ND 4.0) license.

Original Publication Citation

Saglam, A., Papelis, Y. (2023). Efficient maritime object detection and validation for enhancing safety of uncrewed marine systems. Proceedings of the 25th International Conference on Harbor, Maritime and Multimodal Logistic Modeling & Simulation (HMS 2023).,007. https://doi.org/10.46354/i3m.2023.hms.007

Download

Share

COinS