3D Laser scan registration of dual-robot system using vision

Kaushik, Ravi; Xiao, Jizhong; Morris, William; Zhu, Zhigang

doi:10.1109/iros.2009.5354773

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…With the development of 3D sensors and image processing technology, the potential of 3D measurement in pavement detection has earned widespread attention. 3D technologies applied in pavement inspection include 3D structure light [30,31], laser scanning [32], and binocular stereo vision [33]. ere have been attempts to combine 3D techniques and deep learning methods for pavement inspection.…”

Section: D Technology In Pavement Detectionmentioning

confidence: 99%

The Improvement of Automated Crack Segmentation on Concrete Pavement with Graph Network

Jiang

Yuan

Lang

et al. 2022

Journal of Advanced Transportation

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Crack is a common concrete pavement distress that will deteriorate into severe problems without timely repair, which means the automated detection of pavement crack is essential for pavement maintenance. However, automatic crack detection and segmentation remain challenging due to the complex pavement condition. Recent research on pavement crack detection based on deep learning has laid a good foundation for automated crack segmentation, but there can still be improvements. This paper proposes an automatic concrete pavement crack segmentation framework with enhanced graph network branch. First, the nodes of the graph and nodes’ attributions are generated based on the image dividing. The edges of the graph are determined based on Gaussian distribution. Then, the graph from the image is input into the graph branch. The graph feature map of the graph branch output is fused with the image feature map of the encoder and then enters the decoder to recover the image resolution to obtain the crack segmentation result. Finally, the method is tested on a self-built 3D concrete pavement crack dataset. The proposed method achieves the highest F1 and IoU (Intersection over Union) in the comparison experiments. And the graph branch addition improves 0.08 on F1 and 0.06 on IoU compared with U-Net.

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Section: D Technology In Pavement Detectionmentioning

confidence: 99%

The Improvement of Automated Crack Segmentation on Concrete Pavement with Graph Network

Jiang

Yuan

Lang

et al. 2022

Journal of Advanced Transportation

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“…Nuchter [8] exploits parallelism to address the additional computational complexity of 3-D scan registration. Kaushik et al [4] accomplish the 3-D registration of two robots by using external fiducial markings.…”

Section: Prior Workmentioning

confidence: 99%

Fusion of ranging data from robot teams operating in confined areas

2013

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We address the problem of fusing laser ranging data from multiple mobile robots that are surveying an area as part of a robot search and rescue or area surveillance mission. We are specifically interested in the case where members of the robot team are working in close proximity to each other. The advantage of this teamwork is that it greatly speeds up the surveying process; the area can be quickly covered even when the robots use a random motion exploration approach. However, the disadvantage of the close proximity is that it is possible, and even likely, that the laser ranging data from one robot include many depth readings caused by another robot. We refer to this as mutual interference. Using a team of two Pioneer 3-AT robots with tilted SICK LMS-200 laser sensors, we evaluate several techniques for fusing the laser ranging information so as to eliminate the mutual interference. There is an extensive literature on the mapping and localization aspect of this problem. Recent work on mapping has begun to address dynamic or transient objects. Our problem differs from the dynamic map problem in that we look at one kind of transient map feature, other robots, and we know that we wish to completely eliminate the feature. We present and evaluate three different approaches to the map fusion problem: a robot-centric approach, based on estimating team member locations; a map-centric approach, based on inspecting local regions of the map, and a combination of both approaches. We show results for these approaches for several experiments for a two robot team operating in a confined indoor environment .

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“…One of the most precise time of flight measurement systems is based on the laser scanners; however it is the most expensive one. A cheaper variant is the stereo camera, with less precision in the depth estimations [11].…”

Section: Common 3d Perception Techniquesmentioning

confidence: 99%

3D Non-Contact Building Survey Technique

Tamás

Lazea

2013

AEF

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This paper presents a non-contact 3D environments mapping technique using mobile robots with different perception devices such as stereo camera, structural light camera or custom made 3D laser scanner. The custom developed non-contact scanning device is suitable for making building surveillance both in small and large scales. These measurements can be further used for urban region planning as well as for navigation purposes for autonomous agents such as mobile vehicles.. The large maps can be built from successive scan merging in an iterative manner. For such an approach is essential the initial matching between two measurements, which in our proposed method was performed using special extracted features from the measured data sets. The registered maps can be further used for perceptual purposes, including object segmentation or planning maps for the mobile agents. For both types of applications, there are given experimental results obtained from the proposed registration and segmentation algorithms.

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3D Laser scan registration of dual-robot system using vision

Cited by 9 publications

References 11 publications

The Improvement of Automated Crack Segmentation on Concrete Pavement with Graph Network

The Improvement of Automated Crack Segmentation on Concrete Pavement with Graph Network

Fusion of ranging data from robot teams operating in confined areas

3D Non-Contact Building Survey Technique

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