City Research Online

Abstract

This paper introduces a novel Collaborative Si-multaneous Localization and Mapping (CSLAM) framework, enhanced with a Histogram of Oriented Gradients (HOG) de-scriptor, to improve Inter-Map Loop Closure Detection. Our framework stands out by integrating a convolutional neural network-based loop closure detection, employing the HOG de-scriptor for enhanced illumination robustness, and utilizing collaborative mapping from multiple robotic agents for refined pose estimations and mapping precision. Tested in diverse real-world fields, particularly for landmine detection, the framework demonstrates superior robustness and accuracy, outperforming the existing CCM-SLAM model. Additionally, it incorporates a transformation matrix from visual SLAM for LiDAR Point Clouds correction, showcasing its efficacy in 3D mapping and localization in GNSS-denied settings. Our results indicate that incorporating the CALC descriptor within a CSLAM system significantly enhances loop closure detection and mapping precision, marking a significant step forward in autonomous cooperative navigation and mapping in challenging environments

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords:	visual SLAM, dynamic environment, pose estimation, robot vision systems
Subjects:	G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TJ Mechanical engineering and machinery
Departments:	School of Science & Technology School of Science & Technology > Engineering
SWORD Depositor:	Symplectic Administrator

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