Patterns of approximated localised moments for visual loop closure detection
In the context of autonomous mobile robot navigation, loop closing is defined as the correct identification of a previously visited location. Loop closing is essential for the accurate self‐localisation of a robot; however, it is also challenging due to perceptual aliasing, which occurs when the rob...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-04-01
|
| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2016.0237 |
| _version_ | 1797685033190817792 |
|---|---|
| author | Can Erhan Evangelos Sariyanidi Onur Sencan Hakan Temeltas |
| author_facet | Can Erhan Evangelos Sariyanidi Onur Sencan Hakan Temeltas |
| author_sort | Can Erhan |
| collection | DOAJ |
| description | In the context of autonomous mobile robot navigation, loop closing is defined as the correct identification of a previously visited location. Loop closing is essential for the accurate self‐localisation of a robot; however, it is also challenging due to perceptual aliasing, which occurs when the robot traverses in environments with visually similar places (e.g. forests, parks, office corridors). In this study, the authors apply the local Zernike moments (ZMs) for loop closure detection. When computed locally, ZMs provide a high discrimination ability, which enables the distinguishing of similar‐looking places. Particularly, they show that increasing the density over which the local ZMs are computed improves loop closing accuracy significantly. Furthermore, they present an approximation of ZMs that allows the usage of integral images, which enable real‐time operation. Experiments on real datasets with strong perceptual aliasing show that the proposed ZM‐based descriptor outperforms state‐of‐the‐art methods in terms of loop closure accuracy. They also release the source‐code of the implementation for research purposes. |
| first_indexed | 2024-03-12T00:39:25Z |
| format | Article |
| id | doaj.art-af9604aa49244d92bd3c61459183b2e9 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:39:25Z |
| publishDate | 2017-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-af9604aa49244d92bd3c61459183b2e92023-09-15T09:16:45ZengWileyIET Computer Vision1751-96321751-96402017-04-0111323724510.1049/iet-cvi.2016.0237Patterns of approximated localised moments for visual loop closure detectionCan Erhan0Evangelos Sariyanidi1Onur Sencan2Hakan Temeltas3Department of Control and Automation EngineeringIstanbul Technical University34469MaslakIstanbulTurkeySchool of Electronic Engineering and Computer ScienceQueen Mary University of LondonMile End RoadLondonE1 4NSUKDepartment of Control and Automation EngineeringIstanbul Technical University34469MaslakIstanbulTurkeyDepartment of Control and Automation EngineeringIstanbul Technical University34469MaslakIstanbulTurkeyIn the context of autonomous mobile robot navigation, loop closing is defined as the correct identification of a previously visited location. Loop closing is essential for the accurate self‐localisation of a robot; however, it is also challenging due to perceptual aliasing, which occurs when the robot traverses in environments with visually similar places (e.g. forests, parks, office corridors). In this study, the authors apply the local Zernike moments (ZMs) for loop closure detection. When computed locally, ZMs provide a high discrimination ability, which enables the distinguishing of similar‐looking places. Particularly, they show that increasing the density over which the local ZMs are computed improves loop closing accuracy significantly. Furthermore, they present an approximation of ZMs that allows the usage of integral images, which enable real‐time operation. Experiments on real datasets with strong perceptual aliasing show that the proposed ZM‐based descriptor outperforms state‐of‐the‐art methods in terms of loop closure accuracy. They also release the source‐code of the implementation for research purposes.https://doi.org/10.1049/iet-cvi.2016.0237visual loop closure detectionautonomous mobile robot navigationloop closingperceptual aliasingZernike momentsintegral images |
| spellingShingle | Can Erhan Evangelos Sariyanidi Onur Sencan Hakan Temeltas Patterns of approximated localised moments for visual loop closure detection IET Computer Vision visual loop closure detection autonomous mobile robot navigation loop closing perceptual aliasing Zernike moments integral images |
| title | Patterns of approximated localised moments for visual loop closure detection |
| title_full | Patterns of approximated localised moments for visual loop closure detection |
| title_fullStr | Patterns of approximated localised moments for visual loop closure detection |
| title_full_unstemmed | Patterns of approximated localised moments for visual loop closure detection |
| title_short | Patterns of approximated localised moments for visual loop closure detection |
| title_sort | patterns of approximated localised moments for visual loop closure detection |
| topic | visual loop closure detection autonomous mobile robot navigation loop closing perceptual aliasing Zernike moments integral images |
| url | https://doi.org/10.1049/iet-cvi.2016.0237 |
| work_keys_str_mv | AT canerhan patternsofapproximatedlocalisedmomentsforvisualloopclosuredetection AT evangelossariyanidi patternsofapproximatedlocalisedmomentsforvisualloopclosuredetection AT onursencan patternsofapproximatedlocalisedmomentsforvisualloopclosuredetection AT hakantemeltas patternsofapproximatedlocalisedmomentsforvisualloopclosuredetection |