Laser Scan Matching by FAST CVSAC in Dynamic Environment

Islam, Md. Didarul; Reza, S. M. Taslim; Uddin, Jia; Oyekanlu, Emmanuel

doi:10.5815/ijisa.2013.11.02

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Current approaches such as Iterative Closest Point (ICP) [ 8 ], Iterative Matching Range Point (IMRP) [ 9 ], Iterative Dual Correspondence (IDC) [ 9 ], Polar Scan Matching (PSM) [ 3 ], and Iterative Closest Line (ICL) [ 10 ] handle the scan matching problem in an iterative fashion, significantly impacting the amount of time spent on the task. Moreover, the solution convergence is not guaranteed, especially in cases of aggressive manoeuvers or rapid movement due to harsh assignment of correct correspondences [ 11 ]. Furthermore, these approaches suffer from error accumulation over time as well.…”

Section: Introductionmentioning

confidence: 99%

A Novel Real-Time Reference Key Frame Scan Matching Method

Mohamed

Moussa

Elhabiby

et al. 2017

Sensors

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Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions’ environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global approaches. Both approaches suffer from accumulated errors and high processing time due to the iterative nature of the scan matching method. Moreover, point-to-point scan matching is prone to outlier association processes. This paper proposes a low-cost novel method for 2D real-time scan matching based on a reference key frame (RKF). RKF is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches. This algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The algorithm depends on the iterative closest point algorithm during the lack of linear features which is typically exhibited in unstructured environments. The algorithm switches back to the RKF once linear features are detected. To validate and evaluate the algorithm, the mapping performance and time consumption are compared with various algorithms in static and dynamic environments. The performance of the algorithm exhibits promising navigational, mapping results and very short computational time, that indicates the potential use of the new algorithm with real-time systems.

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Section: Introductionmentioning

confidence: 99%

A Novel Real-Time Reference Key Frame Scan Matching Method

Mohamed

Moussa

Elhabiby

et al. 2017

Sensors

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A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

et al. 2020

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In industrial environments, over several decades, Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) have served to improve efficiencies of intralogistics and material handling tasks. However, for system integrators, the choice and effective deployment of improved, suitable and reliable communication and control technologies for these unmanned vehicles remains a very challenging task. Specifics of communication for AGVs and AMRs imposes stringent performance requirements on latency and reliability of communication links which many existing wireless technologies struggle to satisfy. In this paper, a review of latest AGVs and AMRs research results in the past decade is presented. The review encompasses results from different past and present research domains of AGVs. In addition, performance requirements of communication networks in terms of their latencies and reliabilities when they are deployed for AGVs and AMRs coordination, control and fleet management in smart manufacturing environments are discussed. Integration challenges and limitations of present state-of-the-art AGV and AMR technologies when those technologies are used for facilitating AGV-based smart manufacturing and factory of the future applications are also thoroughly discussed. The paper also present a thorough discussion of areas in need of further research regarding the application of 5G networks for AGVs and AMRs fleet management in smart manufacturing environments. In addition, novel integration ideas by which tactile Internet, 5G network slicing and virtual reality applications can be used to facilitate AGV and AMR based factory of the future (FoF) and smart manufacturing applications were motivated.INDEX TERMS Intelligent factory, factory of the future, 5G, smart manufacturing, industry 4.0, autonomous industrial equipment, AGV, AMR, tactile Internet, virtual reality, lean manufacturing.

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