Qualitative vision-based navigation based on sloped funnel lane concept

Kassir, Mohamad Mahdi; Palhang, Maziar; Ahmadzadeh, Mohammad Reza

doi:10.1007/s11370-019-00308-4

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…The results demonstrated improved performance compared to the traditional Extended Kalman Filter. Similarly, many more techniques are available in the literature, including rigid body localization using wireless sensor networks [11,12], WiFi-based localization architecture [13,14], vision-based approaches using different types of cameras [15][16][17], and particle filter-based approaches such as Adaptive Monte Carlo Localization (MCL) [18], Self-Adaptive MCL (SA-MCL) [19], and particle flow filtering architecture [20]. It is also possible to improve their performance with suggested arrangements [21].…”

Section: Introductionmentioning

confidence: 99%

A multi-stage localization framework for accurate and precise docking of autonomous mobile robots (AMRs)

Yilmaz,

Temeltas

2024

Robotica

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Autonomous navigation has been a long-standing research topic, and researchers have worked on many challenging problems in indoor and outdoor environments. One application area of navigation solutions is material handling in industrial environments. With Industry 4.0, the simple problem in traditional factories has evolved into the use of autonomous mobile robots within flexible production islands in a self-decision-making structure. Two main stages of such a navigation system are safe transportation of the vehicle from one point to another and reaching destinations at industrial standards. The main concern in the former is roughly determining the vehicle’s pose to follow the route, while the latter aims to reach the target with high accuracy and precision. Often, it may not be possible or require extra effort to satisfy requirements with a single localization method. Therefore, a multi-stage localization approach is proposed in this study. Particle filter-based large-scale localization approaches are utilized during the vehicle’s movement from one point to another, while scan-matching-based methods are used in the docking stage. The localization system enables the appropriate approach based on the vehicle’s status and task through a decision-making mechanism. The decision-making mechanism uses a similarity metric obtained through the correntropy criterion to decide when and how to switch from large-scale localization to precise localization. The feasibility and performance of the developed method are corroborated through field tests. These evaluations demonstrate that the proposed method accomplishes tasks with sub-centimeter and sub-degree accuracy and precision without affecting the operation of the navigation algorithms in real time.

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Section: Introductionmentioning

confidence: 99%

A multi-stage localization framework for accurate and precise docking of autonomous mobile robots (AMRs)

Yilmaz,

Temeltas

2024

Robotica

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“…Some of them have built-in cameras to handle mapping, planning, localizing and avoiding obstacle issues. There are many pairs of distinctly independent issues such as indoor [6] and outdoor [7] navigations, structured [8] and unstructured [9] environments, qualitative [10] and quantitative [11] image processing, metric [12] and topological [13] reconstruction. They may intertwine in specific research contexts.…”

Section: Introductionmentioning

confidence: 99%

Novel Solution of Topological Recognition of Indoor Objects Based on Optical Flow and Planar Attributes

Anh

2022

AiMT

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An approach of qualitative optical flow processing for indoor object recognition based on planar attributes is presented. The qualitative processing is performed under hierarchical segmentations of optical flow vectors. The proposed solution for indoor object recognition is undertaken from identifying planar and atilt properties of optical flow images. The advantages of the proposed solutions are the use of much simpler arithmetic to obtain more 3D details about indoor objects.

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“…Usually, these methods assume that the first location of the robot is known. The robot is placed at the beginning of the desired visual path to follow it [1,7,8,[11][12][13]. Therefore, they do not use any segment self-localization.…”

Section: Introductionmentioning

confidence: 99%

Two Efficient Visual Methods for Segment Self-localization

2021

Self Cite

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Localization is an essential step in visual navigation algorithms in robotics. Some visual navigation algorithms define the environment through sequential images, which are called visual path. The interval between each consecutive image is called a segment. One crucial step in these kinds of navigation is to find the segment in which the robot is placed (segment selflocalization). Visual segment self-localization methods consist of two stages. In the first stage, a feature matching between the current image of the robot with all the images that form the visual path is done. Usually, in this stage, outliers removal methods such as RANSAC are used after matching to remove the outliers matched features. In the second stage, a segment is chosen depending on the results of the first one. Segment self-localization methods estimate a segment depending just on the percentage of the matched features. This leads to estimate the segment incorrectly in some cases. In this paper, another parameter also is considered to estimate the segment. The parameter is based on the perspective projection model. Moreover, instead of RANSAC which is a stochastic and time consuming method, a more straightforward and more effective method is proposed for outliers detection. The proposed methods are tested on Karlsruhe dataset, and acceptable results are obtained. Also, the methods are compared with three reviewed methods by Nguyen et al. (J Intell Robot Syst 84:217, 2016). Although the proposed methods use a more straightforward outlier method, they give more accurate results.

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Qualitative vision-based navigation based on sloped funnel lane concept

Cited by 5 publications

References 27 publications

A multi-stage localization framework for accurate and precise docking of autonomous mobile robots (AMRs)

A multi-stage localization framework for accurate and precise docking of autonomous mobile robots (AMRs)

Novel Solution of Topological Recognition of Indoor Objects Based on Optical Flow and Planar Attributes

Two Efficient Visual Methods for Segment Self-localization

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