Comparison of Kalman and Interval Approaches for the Simultaneous Localization and Mapping of an Underwater Vehicle

AbstractIn recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

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Research on Laser-Visual Fusion-based Simultaneous Localization and Mapping

Journal of Physics Conference Series ◽

10.1088/1742-6596/1682/1/012049 ◽

2020 ◽

Vol 1682 ◽

pp. 012049

Author(s):

Jianjie Zhenga ◽

Haitao Zhang ◽

Kai Tang ◽

Weidi Kong

Keyword(s):

Simultaneous Localization And Mapping ◽

Localization And Mapping

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Modelling Software Architecture for Visual Simultaneous Localization and Mapping

Automation ◽

10.3390/automation2020003 ◽

2021 ◽

Vol 2 (2) ◽

pp. 48-61

Author(s):

Bhavyansh Mishra ◽

Robert Griffin ◽

Hakki Erhan Sevil

Keyword(s):

System Integration ◽

Ad Hoc ◽

Simultaneous Localization And Mapping ◽

Software Systems ◽

Model Driven ◽

Performance Improvements ◽

Software Model ◽

Localization And Mapping ◽

Stereo Cameras ◽

Core Components

Visual simultaneous localization and mapping (VSLAM) is an essential technique used in areas such as robotics and augmented reality for pose estimation and 3D mapping. Research on VSLAM using both monocular and stereo cameras has grown significantly over the last two decades. There is, therefore, a need for emphasis on a comprehensive review of the evolving architecture of such algorithms in the literature. Although VSLAM algorithm pipelines share similar mathematical backbones, their implementations are individualized and the ad hoc nature of the interfacing between different modules of VSLAM pipelines complicates code reuseability and maintenance. This paper presents a software model for core components of VSLAM implementations and interfaces that govern data flow between them while also attempting to preserve the elements that offer performance improvements over the evolution of VSLAM architectures. The framework presented in this paper employs principles from model-driven engineering (MDE), which are used extensively in the development of large and complicated software systems. The presented VSLAM framework will assist researchers in improving the performance of individual modules of VSLAM while not having to spend time on system integration of those modules into VSLAM pipelines.

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