A Review: Simultaneous Localization and Mapping Algorithms

2015 ◽  
Vol 73 (2) ◽  
Author(s):  
Saif Eddine Hadji ◽  
Suhail Kazi ◽  
Tang Howe Hing ◽  
Mohamed Sultan Mohamed Ali
Keyword(s):  
Autonomous Navigation ◽  
State Of The Art ◽  
Simultaneous Localization And Mapping ◽  
The State ◽  
Sensor Data ◽  
Current Position ◽  
Mapping Algorithms ◽  
The Past ◽  
Localization And Mapping ◽  
Environmental Map

Simultaneous Localization and Mapping (SLAM) involves creating an environmental map based on sensor data, while concurrently keeping track of the robot’s current position. Efficient and accurate SLAM is crucial for any mobile robot to perform robust navigation. It is also the keystone for higher-level tasks such as path planning and autonomous navigation. The past two decades have seen rapid and exciting progress in solving the SLAM problem together with many compelling implementations of SLAM methods. In this paper, we will review the two common families of SLAM algorithms: Kalman filter with its variations and particle filters. This article complements other surveys in this field by reviewing the representative algorithms and the state-of-the-art in each family. It clearly identifies the inherent relationship between the state estimation via the KF versus PF techniques, all of which are derivations of Bayes rule.

Mapping, localization and motion planning in mobile multi-robotic systems

Robotica ◽  
2012 ◽  
Vol 31 (1) ◽  
pp. 1-23 ◽  
Author(s):  
William Rone ◽  
Pinhas Ben-Tzvi
Keyword(s):  
Motion Planning ◽  
System Design ◽  
Motion Control ◽  
State Of The Art ◽  
Simultaneous Localization And Mapping ◽  
Robotic System ◽  
The State ◽  
Robotic Systems ◽  
System Research ◽  
Localization And Mapping

SUMMARYAs researchers have pushed the limits of what can be accomplished by a single robot operating in a known or unknown environment, a greater emphasis has been placed on the utilization of mobile multi-robotic systems to accomplish various objectives. In transitioning from a robot-centric approach to a system-centric approach, considerations must be made for the computational and communicative aspects of the group as a whole, in addition to electromechanical considerations of individual robots. This paper reviews the state-of-the-art of mobile multi-robotic system research, with an emphasis on the confluence of mapping, localization and motion control of robotic system. Methods that compose these three topics are presented, including areas of overlap, such as integrated exploration and simultaneous localization and mapping. From these methods, an analysis of benefits, challenges and tradeoffs associated with multi-robotic system design and use are presented. Finally, specific applications of multi-robotic systems are also addressed in various contexts.

scholarly journals A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2068 ◽  
Author(s):  
César Debeunne ◽  
Damien Vivet
Keyword(s):  
Autonomous Navigation ◽  
Mobile Robotics ◽  
State Of The Art ◽  
Autonomous Underwater Vehicles ◽  
Simultaneous Localization And Mapping ◽  
Underwater Vehicles ◽  
Visual Features ◽  
Localization And Mapping ◽  
Comprehensive Survey ◽  
Self Driving Cars

Autonomous navigation requires both a precise and robust mapping and localization solution. In this context, Simultaneous Localization and Mapping (SLAM) is a very well-suited solution. SLAM is used for many applications including mobile robotics, self-driving cars, unmanned aerial vehicles, or autonomous underwater vehicles. In these domains, both visual and visual-IMU SLAM are well studied, and improvements are regularly proposed in the literature. However, LiDAR-SLAM techniques seem to be relatively the same as ten or twenty years ago. Moreover, few research works focus on vision-LiDAR approaches, whereas such a fusion would have many advantages. Indeed, hybridized solutions offer improvements in the performance of SLAM, especially with respect to aggressive motion, lack of light, or lack of visual features. This study provides a comprehensive survey on visual-LiDAR SLAM. After a summary of the basic idea of SLAM and its implementation, we give a complete review of the state-of-the-art of SLAM research, focusing on solutions using vision, LiDAR, and a sensor fusion of both modalities.

A Simplistic Approach for Lightweight Multi-Agent SLAM Algorithm

2020 ◽  
Vol 11 (3) ◽  
pp. 67-83
Author(s):  
Anton Filatov ◽  
Kirill Krinkin
Keyword(s):  
State Of The Art ◽  
Simultaneous Localization And Mapping ◽  
The State ◽  
Limited Resources ◽  
Challenging Problem ◽  
Multiple Agents ◽  
Localization And Mapping ◽  
Multi Agent ◽  
Slam Algorithm ◽  
Computational Resources

Limitation of computational resources is a challenging problem for moving agents that launch such algorithms as simultaneous localization and mapping (SLAM). To increase the accuracy on limited resources one may add more computing agents that might explore the environment quicker than one and thus to decrease the load of each agent. In this article, the state-of-the-art in multi-agent SLAM algorithms is presented, and an approach that extends laser 2D single hypothesis SLAM for multiple agents is introduced. The article contains a description of problems that are faced in front of a developer of such approach including questions about map merging, relative pose calculation, and roles of agents.

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

scholarly journals The state of the art in soybean transcriptomics resources and gene coexpression networks

2021 ◽  
Author(s):  
Fabricio Almeida-Silva ◽  
Kanhu C Moharana ◽  
Thiago M Venancio
Keyword(s):  
State Of The Art ◽  
The State ◽  
Gene Coexpression Network ◽  
Rna Seq ◽  
Transcriptomic Data ◽  
The Past ◽  
Gene Coexpression ◽  
Genomics Research ◽  
Public Repositories ◽  
Coexpression Networks

Abstract In the past decade, over 3000 samples of soybean transcriptomic data have accumulated in public repositories. Here, we review the state of the art in soybean transcriptomics, highlighting the major microarray and RNA-seq studies that investigated soybean transcriptional programs in different tissues and conditions. Further, we propose approaches for integrating such big data using gene coexpression network and outline important web resources that may facilitate soybean data acquisition and analysis, contributing to the acceleration of soybean breeding and functional genomics research.

The State of the Art in Management Education Applicable to Aerospace Activities

1967 ◽  
Vol 71 (677) ◽  
pp. 342-343
Author(s):  
F. H. East
Keyword(s):  
Research And Development ◽  
Management Education ◽  
State Of The Art ◽  
Development Project ◽  
The State ◽  
The Other ◽  
The Past ◽  
Defence Budget ◽  
The One ◽  
Almost All

The Aviation Group of the Ministry of Technology (formerly the Ministry of Aviation) is responsible for spending a large part of the country's defence budget, both in research and development on the one hand and production or procurement on the other. In addition, it has responsibilities in many non-defence fields, mainly, but not exclusively, in aerospace.Few developments have been carried out entirely within the Ministry's own Establishments; almost all have required continuous co-operation between the Ministry and Industry. In the past the methods of management and collaboration and the relative responsibilities of the Ministry and Industry have varied with time, with the type of equipment to be developed, with the size of the development project and so on. But over the past ten years there has been a growing awareness of the need to put some system into the complex business of translating a requirement into a specification and a specification into a product within reasonable bounds of time and cost.

Cultural Economics: The State of the Art and Perspectives

2021 ◽  
Vol 27 (1) ◽  
pp. 7-32
Author(s):  
Bruce A. Seaman
Keyword(s):  
Industrial Organization ◽  
Intellectual Development ◽  
State Of The Art ◽  
Cultural Economics ◽  
The State ◽  
Sports Economics ◽  
Developmental Patterns ◽  
The Past ◽  
Alternative Perspective ◽  
Core Questions

The intellectual development of cultural economics has exhibited some notable similarities to the challenges faced by researchers pioneering in other areas of economics. While this is not really surprising, previous reviews of this literature have not focused on such patterns. Specifically, the methodology and normative implications of the field of industrial organization and antitrust policy suggest a series of stages identified here as foundation, maturation, reevaluation, and backlash that suggest a way of viewing the development of and controversies surrounding cultural economics. Also, the emerging field of sports economics, which already shares some substantive similarities to the questions addressed in cultural economics, presents a pattern of development by which core questions and principles are identified in a fragmented literature, which then slowly coalesces and becomes consolidated into a more unified literature that essentially reconfirms and extends those earlier core principles. This fragmentation and consolidation pattern is also exhibited by the development of cultural economics. While others could surely suggest different parallels in the search for such developmental patterns, this way of organizing ones thinking about the past and future of this field provides a hoped for alternative perspective on the state of the art of cultural economics.

scholarly journals The Embeddedness of Social Sciences and Economics in Research on Resources

Resources ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 15
Author(s):  
Juan Uribe-Toril ◽  
José Luis Ruiz-Real ◽  
Jaime de Pablo Valenciano
Keyword(s):  
Social Sciences ◽  
State Of The Art ◽  
Local Development ◽  
The State ◽  
Special Issue ◽  
Continuous Review ◽  
The Past ◽  
Research Fields ◽  
Wide Range ◽  
Knowledge Area

Sustainability, local development, and ecology are keywords that cover a wide range of research fields in both experimental and social sciences. The transversal nature of this knowledge area creates synergies but also divergences, making a continuous review of the existing literature necessary in order to facilitate research. There has been an increasing number of articles that have analyzed trends in the literature and the state-of-the-art in many subjects. In this Special Issue of Resources, the most prestigious researchers analyzed the past and future of Social Sciences in Resources from an economic, social, and environmental perspective.

scholarly journals Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 37 ◽  
Author(s):  
Luca Cappelletti ◽  
Tommaso Fontana ◽  
Guido Walter Di Donato ◽  
Lorenzo Di Tucci ◽  
Elena Casiraghi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Missing Data ◽  
State Of The Art ◽  
The State ◽  
Complex Data ◽  
Data Imputation ◽  
Genome Sequences ◽  
Missing Data Imputation ◽  
The Past ◽  
Learning Techniques

Missing data imputation has been a hot topic in the past decade, and many state-of-the-art works have been presented to propose novel, interesting solutions that have been applied in a variety of fields. In the past decade, the successful results achieved by deep learning techniques have opened the way to their application for solving difficult problems where human skill is not able to provide a reliable solution. Not surprisingly, some deep learners, mainly exploiting encoder-decoder architectures, have also been designed and applied to the task of missing data imputation. However, most of the proposed imputation techniques have not been designed to tackle “complex data”, that is high dimensional data belonging to datasets with huge cardinality and describing complex problems. Precisely, they often need critical parameters to be manually set or exploit complex architecture and/or training phases that make their computational load impracticable. In this paper, after clustering the state-of-the-art imputation techniques into three broad categories, we briefly review the most representative methods and then describe our data imputation proposals, which exploit deep learning techniques specifically designed to handle complex data. Comparative tests on genome sequences show that our deep learning imputers outperform the state-of-the-art KNN-imputation method when filling gaps in human genome sequences.

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