Landmark Placement Optimization for Accurate Localization in Autonomous Vehicles

Moreno, Francisco Miguel; Hussein, Ahmed; García, Fernando

doi:10.1109/itsc48978.2021.9564926

Cited by 3 publications

(1 citation statement)

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“…Computer vision systems can be used to locate mobile robots and this occurs through the identification of landmarks and QRcodes in the environment. Regarding the use of landmarks, it consists of identifying the distance and angle of the robot in relation to a known object (reference) [18]. Localization using QRcode (quick response code) is similar to this concept, considering that the robot needs a camera to identify QRcodes positioned in the environment [19].…”

Section: Introductionmentioning

confidence: 99%

Proposal of Simultaneous Localization and Mapping for Mobile Robots Indoor Environments Using Petri Nets and Computer Vision

Mota,

Batista,

Alexandria

2024

Preprint

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Studies in the area of mobile robotics have advanced in recent years, mainly due to the evolution of technology and the growing need for automated and dynamic solutions in sectors such as industry, transport and agriculture. These devices are complex and the ideal method for localizing, mapping and navigating autonomous mobile robots changes depending on the application. Thus, the general objective of this work is to propose a simultaneous localization and mapping method for autonomous mobile robots in indoor environments, using Computer Vision (CV) and Petri Net (PN). A landmark was placed next to each door in the analyzed region and images were acquired as the rooms in the environment were explored. The algorithm processes the images to count and identify the doors. A transition is created in the PN for each door found and the rooms connected by these doors are represented by the places in the PN. Then, one of the doors is crossed, new images are obtained and the process is repeated until all rooms are explored. The algorithm generates an PN, which can be represented by an image file (.png) and a file with the extension .pnml. The results compare the layout of four environments with the respective generated PNs. Furthermore, six evaluation criteria are proposed for validating Petri nets as a topological map of environments. It is concluded that using PN for this purpose presents originality and potential innovation, being a SLAM technique for indoor environments, which demands low computational cost.

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Section: Introductionmentioning

confidence: 99%

Proposal of Simultaneous Localization and Mapping for Mobile Robots Indoor Environments Using Petri Nets and Computer Vision

Mota,

Batista,

Alexandria

2024

Preprint

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Proposal of simultaneous localization and mapping for mobile robots indoor environments using Petri nets and computer vision

Mota,

Batista,

Alexandria

2024

Int J Adv Manuf Technol

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Navigating the Future: AI Innovations for Intelligent Mobility in Smart Cities

Al-Kaff

2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">As we stand on the evolution of a new era in urban development, the concept of smart cities emerges as an embodiment of innovation and progress. A smart city uses cutting-edge technologies to improve the quality, performance and interactivity of urban services, optimize resources, and foster sustainable and well-connected societies. A critical component within this paradigm is the domain of transportation, where the application of artificial intelligence is fundamentally transforming how we navigate our cities. In this paper, we conduct a comprehensive exploration of the evolving landscape of AI within the context of intelligent mobility and smart cities, with a specific focus on our contributions in this field. This investigation not only highlights the advances in this field but also focuses specifically on the significant contributions of Autonomous Mobility and Perception Lab (AMPL) to deploying AI in developing intelligent mobility solutions. As urban areas worldwide grapple with the complexities of growing populations and the increasing demand for efficient, sustainable transportation, the critical role of implementing smart city concepts, especially intelligent mobility systems, becomes evident. This exploration is not merely a review of our strides; it is a voyage into the future, showing how AI continues to reshape urban transportation within the smart city paradigm.</div></div>

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Landmark Placement Optimization for Accurate Localization in Autonomous Vehicles

Cited by 3 publications

References 219 publications

Proposal of Simultaneous Localization and Mapping for Mobile Robots Indoor Environments Using Petri Nets and Computer Vision

Proposal of Simultaneous Localization and Mapping for Mobile Robots Indoor Environments Using Petri Nets and Computer Vision

Proposal of simultaneous localization and mapping for mobile robots indoor environments using Petri nets and computer vision

Navigating the Future: AI Innovations for Intelligent Mobility in Smart Cities

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