Edge detection using structured laser pattern and vision for mobile robot navigation

Xie, Yang; Wang, Wei; Guo, Jiajie; Lee, Kok-Meng

doi:10.1109/aim.2011.6027155

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…At the end of the development the code must run on an embedded system or on an embedded computer. [37] The calculation throughput these hardware elements is limited, so the code most be speed optimized. [39] The program details have different priorities.…”

Section: Calculation Methodsmentioning

confidence: 99%

Variable Robot Geometry Optimization Method to Avoid Tip Over Situations During Slow Motion on Unknown Terrains

Tajti¹,

Kovács²,

Szayer³

et al. 2016

AARMS

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This paper presents a parametrized stability control method for special slow motion field mobile robots, based on use cases from border surveillance. The concept uses the centre of gravity (COG) as the virtual centre of motion (VCM). The simplified robot geometry is an input parameter of the model, so it can work with different types of mobile robots, like holonomic-wheeled, differential-wheeled, steered, tracked, wheeled-tracked, segmented, etc. structures. This method resulted in the implementation of a flexible and universal control algorithm for transformable and hybrid drive mobile robots, where every parameter can be changed and recalculated for different applications or even in discrete time steps during the motion at a 3D path. The velocity reference, the angular velocity reference and the optimization parameter (for example gravity compensation) of the robot can be prescribed. The model was implemented in MATLAB and can be compiled to C for measurements and validation with test robots.

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Section: Calculation Methodsmentioning

confidence: 99%

Variable Robot Geometry Optimization Method to Avoid Tip Over Situations During Slow Motion on Unknown Terrains

Tajti¹,

Kovács²,

Szayer³

et al. 2016

AARMS

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“…In [7] particularly for autonomous steel bridge inspection robot, the authors proposed a task-level primitive and online navigation combining with IR sensor, which helps the robot move in local area. In [8], the authors proposed a method to detect edges on a large surface, which is used in navigation. The method in [9] supported the small size robot to move in a large-inside steel bridge space.…”

Section: Related Workmentioning

confidence: 99%

Control Framework for a Hybrid-steel Bridge Inspection Robot

Bui

Nguyen

Billah

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Autonomous navigation is essential for steel bridge inspection robot to monitor and maintain the working condition of steel bridge. Majority of existing robotic solutions requires human support to navigate the robot doing the inspection. In this paper, a navigation framework is proposed for ARA robot [1], [2] to run on mobile mode. In this mode, the robot needs to cross and inspect all the available steel bars. The most significant contributions of this research are four algorithms, which can process the depth data, segment it into clusters, estimate the boundaries, construct a graph to represent the structure, generate a shortest inspection path with any starting and ending points, and determine available robot configuration for path planning. Experiments on steel bridge structures setup highlight the effective performance of the algorithms, and the potential to apply to the ARA robot to run on real bridge structures. We released our source code in Github for the research community to use.

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“…An efficient edge detector is necessary to the extraction of object contours from depth edges. Edge detector is significant in various depth scenes [1] such as target recognition [2], human posture recognition [3], [4], and robot navigation [5]- [7]. In contemporary applications, depth images can be acquired through the use of time-of-flight (TOF) cameras [8], [9], binocular cameras [10], structured light cameras [11] and through light detection and ranging(LIDAR) [12], [13].…”

Section: Introductionmentioning

confidence: 99%

LBP-Based Edge Detection Method for Depth Images With Low Resolutions

et al. 2019

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Depth edge represents contours of depth images. However, captured images often have low resolution. Furthermore, conventional edge detection methods fail to handle depth images with low resolution. To cater depth images with low resolution, we propose a novel edge detection method with high accuracy and computational speed. The proposed method modifies the local binary pattern and selects an adaptive threshold to obtain a rough edge map. Then, it removes irrelevant edges to obtain the result. Experimental results show that the proposed method can rapidly acquire accurate depth edge maps and is applicable to depth images with low resolution. The proposed method is compared with and outperforms other renowned edge detection methods.

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Edge detection using structured laser pattern and vision for mobile robot navigation

Cited by 5 publications

References 18 publications

Variable Robot Geometry Optimization Method to Avoid Tip Over Situations During Slow Motion on Unknown Terrains

Variable Robot Geometry Optimization Method to Avoid Tip Over Situations During Slow Motion on Unknown Terrains

Control Framework for a Hybrid-steel Bridge Inspection Robot

LBP-Based Edge Detection Method for Depth Images With Low Resolutions

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