Convolutional Auto-Encoder and Independent Component Analysis Based Automatic Place Recognition for Moving Robot in Invariant Season Condition
Abstract Building up a map is essential for mobile robots to localize their position and perfect autonomous navigation which is known as Simultaneous Localization and Mapping (SLAM). The map has become very important when the weather is inappropriate for the robot. However, the map becomes inconsist...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2022-12-01
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| Series: | Human-Centric Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44230-022-00013-z |
| _version_ | 1827770504747941888 |
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| author | Md. Tariqul Islam Khan Md. Hasib Md. Mahbubur Rahman Abdur Nur Tusher Mohammad Shafiul Alam Md. Rafiqul Islam |
| author_facet | Md. Tariqul Islam Khan Md. Hasib Md. Mahbubur Rahman Abdur Nur Tusher Mohammad Shafiul Alam Md. Rafiqul Islam |
| author_sort | Md. Tariqul Islam |
| collection | DOAJ |
| description | Abstract Building up a map is essential for mobile robots to localize their position and perfect autonomous navigation which is known as Simultaneous Localization and Mapping (SLAM). The map has become very important when the weather is inappropriate for the robot. However, the map becomes inconsistent when the robot moves in the environment and detects errors in its detection accuracy. The robot had difficulty identifying its previously visited path, which is called loop-closure detection when the climate changed immensely e.g. seasonal changes. The main goal of this work is to apply Independent Component Analysis (ICA) and Auto-Encoder (Convolutional Auto-Encoder and Fundamental Auto-Encoder) to understand the route through the robot. During the operation of robots across a wide range of environmental changing conditions, the ICA has auspicious potential to extract descriptors of condition-invariant images. On the other hand, Auto-Encoder has the capability to differentiate condition variant and condition invariant characteristics of a site and identify the most possible route for the robot. In order to complete this work perfectly, we used three seasonal datasets, they are Summer–Fall, Spring–Fall, and Summer–Spring datasets. This work uses the baseline method with a precision-recall curve and evaluates the performance of our proposed algorithm, especially the ICA algorithm. In short, the proposed algorithm ICA showed a 91.05% accuracy rate which is better than the baseline algorithm. |
| first_indexed | 2024-03-11T12:40:28Z |
| format | Article |
| id | doaj.art-444ab9e3b17340bc8f424c2f5805608a |
| institution | Directory Open Access Journal |
| issn | 2667-1336 |
| language | English |
| last_indexed | 2024-03-11T12:40:28Z |
| publishDate | 2022-12-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Human-Centric Intelligent Systems |
| spelling | doaj.art-444ab9e3b17340bc8f424c2f5805608a2023-11-05T12:20:23ZengSpringer NatureHuman-Centric Intelligent Systems2667-13362022-12-0131132410.1007/s44230-022-00013-zConvolutional Auto-Encoder and Independent Component Analysis Based Automatic Place Recognition for Moving Robot in Invariant Season ConditionMd. Tariqul Islam0Khan Md. Hasib1Md. Mahbubur Rahman2Abdur Nur Tusher3Mohammad Shafiul Alam4Md. Rafiqul Islam5Department of Electronics and Communication Engineering, Khulna University of Engineering and TechnologyDepartment of Computer Science and Engineering, Ahsanullah University of Science and TechnologyDepartment of Civil Engineering, European University of BangladeshDepartment of Computer Science and Engineering, Daffodil International UniversityDepartment of Computer Science and Engineering, Ahsanullah University of Science and TechnologyData Science Institute (DSI), University of Technology Sydney (UTS)Abstract Building up a map is essential for mobile robots to localize their position and perfect autonomous navigation which is known as Simultaneous Localization and Mapping (SLAM). The map has become very important when the weather is inappropriate for the robot. However, the map becomes inconsistent when the robot moves in the environment and detects errors in its detection accuracy. The robot had difficulty identifying its previously visited path, which is called loop-closure detection when the climate changed immensely e.g. seasonal changes. The main goal of this work is to apply Independent Component Analysis (ICA) and Auto-Encoder (Convolutional Auto-Encoder and Fundamental Auto-Encoder) to understand the route through the robot. During the operation of robots across a wide range of environmental changing conditions, the ICA has auspicious potential to extract descriptors of condition-invariant images. On the other hand, Auto-Encoder has the capability to differentiate condition variant and condition invariant characteristics of a site and identify the most possible route for the robot. In order to complete this work perfectly, we used three seasonal datasets, they are Summer–Fall, Spring–Fall, and Summer–Spring datasets. This work uses the baseline method with a precision-recall curve and evaluates the performance of our proposed algorithm, especially the ICA algorithm. In short, the proposed algorithm ICA showed a 91.05% accuracy rate which is better than the baseline algorithm.https://doi.org/10.1007/s44230-022-00013-zAuto-encoderIndependent Component AnalysisMachine learningDeep learningPrinciple Component AnalysisSLAM |
| spellingShingle | Md. Tariqul Islam Khan Md. Hasib Md. Mahbubur Rahman Abdur Nur Tusher Mohammad Shafiul Alam Md. Rafiqul Islam Convolutional Auto-Encoder and Independent Component Analysis Based Automatic Place Recognition for Moving Robot in Invariant Season Condition Human-Centric Intelligent Systems Auto-encoder Independent Component Analysis Machine learning Deep learning Principle Component Analysis SLAM |
| title | Convolutional Auto-Encoder and Independent Component Analysis Based Automatic Place Recognition for Moving Robot in Invariant Season Condition |
| title_full | Convolutional Auto-Encoder and Independent Component Analysis Based Automatic Place Recognition for Moving Robot in Invariant Season Condition |
| title_fullStr | Convolutional Auto-Encoder and Independent Component Analysis Based Automatic Place Recognition for Moving Robot in Invariant Season Condition |
| title_full_unstemmed | Convolutional Auto-Encoder and Independent Component Analysis Based Automatic Place Recognition for Moving Robot in Invariant Season Condition |
| title_short | Convolutional Auto-Encoder and Independent Component Analysis Based Automatic Place Recognition for Moving Robot in Invariant Season Condition |
| title_sort | convolutional auto encoder and independent component analysis based automatic place recognition for moving robot in invariant season condition |
| topic | Auto-encoder Independent Component Analysis Machine learning Deep learning Principle Component Analysis SLAM |
| url | https://doi.org/10.1007/s44230-022-00013-z |
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