scholarly journals Modelling Software Architecture for Visual Simultaneous Localization and Mapping

Automation ◽  
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.

Model-driven software systems engineering in robotics: Covering the complete life-cycle of a robot

2015 ◽  
Vol 57 (2) ◽  
Author(s):  
Christian Schlegel ◽  
Alex Lotz ◽  
Matthias Lutz ◽  
Dennis Stampfer ◽  
Juan F. Inglés-Romero ◽  
...  
Keyword(s):  
Systems Engineering ◽  
System Integration ◽  
Software Systems ◽  
Model Driven ◽  
Domain Specific ◽  
Complete Life Cycle ◽  
Systems Software ◽  
Model Driven Software Development ◽  
Component Based Software Engineering ◽  
Software Systems Engineering

AbstractRobotic systems are complex, software intensive and heterogeneous composite systems. Software systems engineering and system integration is still a major challenge in robotics. We describe how component based software engineering (CBSE), model-driven software development (MDSD) and domain-specific languages (DSLs) for variability management complement each other in addressing the robotics software challenge. We outline how these approaches pave the way towards a software business ecosystem in robotics. We put a focus onto challenges still being considered as open and worth being addressed next.

scholarly journals A Process Model for Component-Based Model-Driven Software Development

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 302
Author(s):  
Afrah Umran Alrubaee ◽  
Deniz Cetinkaya ◽  
Gernot Liebchen ◽  
Huseyin Dogan
Keyword(s):  
Software Development ◽  
Process Model ◽  
Development Process ◽  
Ad Hoc ◽  
Learning System ◽  
Software Systems ◽  
Software Development Process ◽  
Model Driven ◽  
Model Driven Software Development ◽  
Component Based Software Engineering

Developing high quality, reliable and on time software systems is challenging due to the increasing size and complexity of these systems. Traditional software development approaches are not suitable for dealing with such challenges, so several approaches have been introduced to increase the productivity and reusability during the software development process. Two of these approaches are Component-Based Software Engineering (CBSE) and Model-Driven Software Development (MDD) which focus on reusing pre-developed code and using models throughout the development process respectively. There are many research studies that show the benefits of using software components and model-driven approaches. However, in many cases the development process is either ad-hoc or not well-defined. This paper proposes a new software development process model that merges CBSE and MDD principles to facilitate software development. The model is successfully tested by applying it to the development of an e-learning system as an exemplar case study.

scholarly journals MAPPING AND POSITIONING SYSTEM ON OMNIDIRECTIONAL ROBOT USING SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) METHOD BASED ON LIDAR

2021 ◽  
Vol 83 (6) ◽  
pp. 41-52
Author(s):  
Achmad Akmal Fikri ◽  
Lilik Anifah
Keyword(s):  
Monte Carlo ◽  
System Integration ◽  
Simultaneous Localization And Mapping ◽  
Autonomous Robot ◽  
Positioning System ◽  
Adaptive Monte Carlo ◽  
Surrounding Environment ◽  
Localization And Mapping ◽  
Mapping System ◽  
Monte Carlo Localization

The main problem from autonomous robot for navigation is how the robot able to recognize the surrounding environment and know this position. These problems make this research weakness and become a challenge for further research. Therefore, this research focuses on designing a mapping and positioning system using Simultaneous Localization and Mapping (SLAM) method which is implemented on an omnidirectional robot using a LiDAR sensor. The proposes of this research  are mapping system using the google cartographer algorithm combined with the eulerdometry method, eulerdometry is a combination of odometry and euler orientation from IMU sensor, while the positioning system uses the Adaptive Monte Carlo Localization (AMCL) method combined with the eulerdometry method. Testing is carried out by testing the system five times from each system, besides that testing is also carried out at each stage, testing on each sensor used such as the IMU and LiDAR sensors, and testing on system integration, including the eulerdometry method, mapping system and positioning system. The results on the mapping system showed optimal results, even though there was still noise in the results of the maps created, while the positioning system test got an average RMSE value from each map created of 278.55 mm on the x-axis, 207.37 mm on the y-axis, and 4.28o on the orientation robot.

scholarly journals A survey: which features are required for dynamic visual simultaneous localization and mapping?

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zewen Xu ◽  
Zheng Rong ◽  
Yihong Wu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Dynamic Environments ◽  
Visual Features ◽  
Advantages And Disadvantages ◽  
Intelligent Robots ◽  
Localization And Mapping ◽  
High Level ◽  
Static World ◽  
Robotic Applications ◽  
Significant Attention

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.

scholarly journals Software model for studying the features of wireless connections in Flying Ad-Hoc Networks (FANETs)

2021 ◽  
Vol 1840 (1) ◽  
pp. 012024
Author(s):  
I Puleko ◽  
O Svintsytska ◽  
O Vlasenko ◽  
V Chumakevych
Keyword(s):  
Ad Hoc Networks ◽  
Ad Hoc ◽  
Software Model ◽  
Hoc Networks

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

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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