A Review: Simultaneous Localization and Mapping in Application to Autonomous Robot

Agunbiade, Olusanya Y.; Zuva, Tranos

doi:10.20944/preprints201805.0293.v1

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…Navigation in structured and unstructured environments is a central activity in many robotics applications. The simultaneous localization and mapping (SLAM) [1] problem has been recurrently studied by researchers around the world in the last three decades. The problem is essentially creating (updating) a map of an unknown environment along with the robot poses concurrently.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Augmented Vision-Based Ellipsoidal SLAM for Indoor Environments

Lahemer

Rad

2019

Sensors

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In this paper, the problem of Simultaneous Localization And Mapping (SLAM) is addressed via a novel augmented landmark vision-based ellipsoidal SLAM. The algorithm is implemented on a NAO humanoid robot and is tested in an indoor environment. The main feature of the system is the implementation of SLAM with a monocular vision system. Distinguished landmarks referred to as NAOmarks are employed to localize the robot via its monocular vision system. We henceforth introduce the notion of robotic augmented reality (RAR) and present a monocular Extended Kalman Filter (EKF)/ellipsoidal SLAM in order to improve the performance and alleviate the computational effort, to provide landmark identification, and to simplify the data association problem. The proposed SLAM algorithm is implemented in real-time to further calibrate the ellipsoidal SLAM parameters, noise bounding, and to improve its overall accuracy. The augmented EKF/ellipsoidal SLAM algorithms are compared with the regular EKF/ellipsoidal SLAM methods and the merits of each algorithm is also discussed in the paper. The real-time experimental and simulation studies suggest that the adaptive augmented ellipsoidal SLAM is more accurate than the conventional EKF/ellipsoidal SLAMs.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Augmented Vision-Based Ellipsoidal SLAM for Indoor Environments

Lahemer

Rad

2019

Sensors

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Historical and futuristic perspectives of robotics

Zhao

et al. 2020

Artif Life Robotics

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

Agunbiade

2024

Int J Intell Robot Appl

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Simultaneous 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.

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A Review: Simultaneous Localization and Mapping in Application to Autonomous Robot

Cited by 7 publications

References 27 publications

An Adaptive Augmented Vision-Based Ellipsoidal SLAM for Indoor Environments

An Adaptive Augmented Vision-Based Ellipsoidal SLAM for Indoor Environments

Historical and futuristic perspectives of robotics

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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