Enhancing the MCL-SLAM algorithm to overcome the issue of illumination variation, non-static environment and kidnapping to present the NIK-SLAM

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dc.contributor.authorAgunbiade, Olusanya Yinka
dc.date.accessioned2025-01-21T12:26:23Z
dc.date.available2025-01-21T12:26:23Z
dc.date.issued2024
dc.description.abstractSimultaneous Localization and Mapping (SLAM) is a major challenge that has been comprehensively investigated by scholars. SLAM’s ability to support autonomous navigation helps the robot to move from one place to another without being controlled by humans. This achievement has attracted the attention of scholars towards focusing on this area. Over the decades, countless procedures have been developed with extraordinary accomplishments. However, some problems can degrade the efficiency of SLAM (Simultaneous Localization and Mapping) technique, and examples of such problems include environmental noises (light intensity and shadow), non-static environment, and kidnap robots. These problems create inconsistency in measurement that can lead to erroneous navigation for an unmanned vehicle. In minimizing the effect of these problems, a novel SLAM technique based on several re-modifications of MCL is presented in this paper. This innovative technique is known as NIK-SLAM. The NIK-SLAM is equipped with filters alongside some modifications of the original Monte-Carlo algorithm (MCL) to maximize its performance. The filters were employed to overcome the effect of shadow and light intensity while the re-modification of MCL is used to overcome the limitation of kidnapping and non-static objects. A publicly available dataset (TUM-RGBD) and a private dataset were used for evaluation. Matlab was used for simulation while assessments were based on quantitative and qualitative validation. The results demonstrated a better performance of the NIK-SLAM, attaining a lower trajectory error for most experiments when compared to the original MCL and other SLAM algorithms found in the literature, but at the expense of delayed processing time. Furthermore, this accomplishment supports pathway planning, and exploration into unstable environments while it decreases accident rates/human fatalities, and in the future study, the issue of delayed processing time will be addressed.
dc.identifier.citationAgunbiade, O.Y., 2024. Enhancing the MCL-SLAM algorithm to overcome the issue of illumination variation, non-static environment and kidnapping to present the NIK-SLAM. International Journal of Intelligent Robotics and Applications, pp.1-22.
dc.identifier.urihttps://doi.org/10.1007/s41315-024-00396-1
dc.identifier.urihttps://hdl.handle.net/10566/19876
dc.language.isoen
dc.publisherSpringer
dc.subjectAutonomous robot
dc.subjectFiltering algorithm
dc.subjectKidnap problem
dc.subjectNavigation
dc.subjectNon-static environment
dc.titleEnhancing the MCL-SLAM algorithm to overcome the issue of illumination variation, non-static environment and kidnapping to present the NIK-SLAM
dc.typeArticle

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