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pro vyhledávání: '"Stathoulopoulos, Nikolaos"'
Autor:
Stathoulopoulos, Nikolaos, Lindqvist, Björn, Koval, Anton, Agha-mohammadi, Ali-akbar, Nikolakopoulos, George
In this article, a novel approach for merging 3D point cloud maps in the context of egocentric multi-robot exploration is presented. Unlike traditional methods, the proposed approach leverages state-of-the-art place recognition and learned descriptor
Externí odkaz:
http://arxiv.org/abs/2404.18006
In the field of resource-constrained robots and the need for effective place recognition in multi-robotic systems, this article introduces RecNet, a novel approach that concurrently addresses both challenges. The core of RecNet's methodology involves
Externí odkaz:
http://arxiv.org/abs/2402.02192
This paper presents a framework addressing the challenge of global localization in autonomous mobile robotics by integrating LiDAR-based descriptors and Wi-Fi fingerprinting in a pre-mapped environment. This is motivated by the increasing demand for
Externí odkaz:
http://arxiv.org/abs/2310.06384
Change detection and irregular object extraction in 3D point clouds is a challenging task that is of high importance not only for autonomous navigation but also for updating existing digital twin models of various industrial environments. This articl
Externí odkaz:
http://arxiv.org/abs/2306.15416
Autor:
Kyuroson, Alexander, Dahlquist, Niklas, Stathoulopoulos, Nikolaos, Viswanathan, Vignesh Kottayam, Koval, Anton, Nikolakopoulos, George
Algorithms for autonomous navigation in environments without Global Navigation Satellite System (GNSS) coverage mainly rely on onboard perception systems. These systems commonly incorporate sensors like cameras and Light Detection and Rangings (LiDAR
Externí odkaz:
http://arxiv.org/abs/2304.14520
This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots' poses. The no
Externí odkaz:
http://arxiv.org/abs/2301.09213
Current global re-localization algorithms are built on top of localization and mapping methods andheavily rely on scan matching and direct point cloud feature extraction and therefore are vulnerable infeatureless demanding environments like caves and
Externí odkaz:
http://arxiv.org/abs/2210.07285
Publikováno v:
In Expert Systems With Applications 1 March 2024 237 Part B
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