A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently
In this paper we describe a <italic>scan-matching based registration algorithm</italic> for tracking moving objects which falls in the emerging area that predicates the integration between robotics and <italic>big data applications</italic>. The scan matching approaches track...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9462101/ |
| _version_ | 1818480038333382656 |
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| author | Kristijan Lenac Alfredo Cuzzocrea Enzo Mumolo |
| author_facet | Kristijan Lenac Alfredo Cuzzocrea Enzo Mumolo |
| author_sort | Kristijan Lenac |
| collection | DOAJ |
| description | In this paper we describe a <italic>scan-matching based registration algorithm</italic> for tracking moving objects which falls in the emerging area that predicates the integration between robotics and <italic>big data applications</italic>. The scan matching approaches track paths of a mobile object by comparing maps of the environment seen by the object during its movement. Algorithms described in this paper are hybrid, i.e. they compare maps by using first a genetic pre-alignment based on a novel metrics, and then performing a finer alignment using a deterministic approach. This kind of hybridization is, indeed, not new. However, the novel metrics used in this paper leads to important new properties, namely to correct arbitrary rotational errors and to cover larger search spaces. The proposed algorithm is experimentally compared to other approaches, and better performance in terms of accuracy and robustness are reported. Finally, our algorithm is also very fast thanks to the genetic pre-alignment task and the novel metrics we propose. |
| first_indexed | 2024-12-10T11:17:48Z |
| format | Article |
| id | doaj.art-0c9566f0d30e4ddeb467f38f1bddfdfb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T11:17:48Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0c9566f0d30e4ddeb467f38f1bddfdfb2022-12-22T01:51:05ZengIEEEIEEE Access2169-35362021-01-019917419175310.1109/ACCESS.2021.30915209462101A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and EfficientlyKristijan Lenac0https://orcid.org/0000-0003-0201-4177Alfredo Cuzzocrea1https://orcid.org/0000-0002-7104-6415Enzo Mumolo2https://orcid.org/0000-0001-9399-0370Faculty of Engineering, University of Rijeka, Rijeka, CroatiaIDEA Lab, University of Calabria, Rende, ItalyDepartment of Engineering and Architecture, University of Trieste, Trieste, ItalyIn this paper we describe a <italic>scan-matching based registration algorithm</italic> for tracking moving objects which falls in the emerging area that predicates the integration between robotics and <italic>big data applications</italic>. The scan matching approaches track paths of a mobile object by comparing maps of the environment seen by the object during its movement. Algorithms described in this paper are hybrid, i.e. they compare maps by using first a genetic pre-alignment based on a novel metrics, and then performing a finer alignment using a deterministic approach. This kind of hybridization is, indeed, not new. However, the novel metrics used in this paper leads to important new properties, namely to correct arbitrary rotational errors and to cover larger search spaces. The proposed algorithm is experimentally compared to other approaches, and better performance in terms of accuracy and robustness are reported. Finally, our algorithm is also very fast thanks to the genetic pre-alignment task and the novel metrics we propose.https://ieeexplore.ieee.org/document/9462101/Moving objectsscan-matching algorithmsintelligent systemsgenetic optimization |
| spellingShingle | Kristijan Lenac Alfredo Cuzzocrea Enzo Mumolo A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently IEEE Access Moving objects scan-matching algorithms intelligent systems genetic optimization |
| title | A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently |
| title_full | A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently |
| title_fullStr | A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently |
| title_full_unstemmed | A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently |
| title_short | A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently |
| title_sort | novel genetic scan matching based registration algorithm for supporting moving objects tracking effectively and efficiently |
| topic | Moving objects scan-matching algorithms intelligent systems genetic optimization |
| url | https://ieeexplore.ieee.org/document/9462101/ |
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