Sonar Sensor Interpretation and Infrared Image Fusion for Mobile Robotics

Hinders, Mark K.; Gao, Wen; Fehlman, William L.

doi:10.5772/4768

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…At this time, ŝ is a sparse signal, and needs to be de-sparsed to obtain a reconstructed signal x. Common reconstruction and recovery algorithms are mainly divided into greedy algorithms and convex optimisation algorithms [24,25]. Although the number of observations in the convex optimisation algorithm is small, the computational complexity is higher than other algorithms: the greedy algorithm is easy to operate, has a fast recovery speed, and has a wide range of applications, but The sparsity of the reconstructed signal needs to be analysed before using the greedy algorithm.…”

Section: Refactoring Recoverymentioning

confidence: 99%

Application of matrix multiplication in signal sensor image perception

Dai

Cheng

Wang³

et al. 2022

Applied Mathematics and Nonlinear Sciences

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LOT wireless sensor nodes are limited by physical factors, usually have weak computing power and endurance, and wireless communication methods are very vulnerable to information theft. Therefore, it is of great significance to ensure the safe and efficient transmission of images in new application scenarios. In view of the need for an efficient image transmission, this paper combines compressed sensing technology with p-tensor product theory, applies the above theory to distributed wireless sensor networks, and uses the correlation of adjacent sensor nodes in wireless sensor networks to propose an improved a joint sparse model for measurement matrices and reduction algorithms. The feasibility is verified by simulation experiments, and the comparison between joint reconstruction and single reconstruction, and the application of various algorithms in other algorithms is carried out, and the actual completion time and storage capacity are analysed. The minimum completion time for wavelet transform is 1.29, the sparse estimated time for the selection of preliminary P waves is 0.07 and the compressed sensing time is 0.20. The maximum completion time for wavelet transform was 1.32, for sparse estimation, it is 0.62, for preliminary P-wave selection, it is 0.17, and for compressed sensing, it is 0.88. The processing time is no >3 s and the runtime is only 0.22–0.88 s. The results show that compared with the compressed sensing of a single node, the joint sparse model based on distributed compressed sensing has a smaller reconstruction error, and can achieve high-precision signal reconstruction when the measurement value is small.

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Section: Refactoring Recoverymentioning

confidence: 99%

Application of matrix multiplication in signal sensor image perception

Dai

Cheng

Wang³

et al. 2022

Applied Mathematics and Nonlinear Sciences

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“…Regarding the form of the sound's field, what is important is the report between the source's dimmension and the wave length of the output singnal. [3] Received echo signal can be evaluated in terms of process emission properties / reflection: a) by measuring time t elapsed between the issuing and receiving audible echo of the controlled object, one can determine the distance d between sensor and obstacle [4]: (1) where:…”

Section: Theoretical Considerationsmentioning

confidence: 99%

Object Detection with Ultrasound Sensors in Mobile Robots Working Area

Mihai-Alin¹,

Dolga²

2012

AMM

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The ultrasound sensor imitates the bats and the other animals’ abilities to use ultrasound frequencies for navigation in order to determine the distance between them and the obstacles. These sensors are recommended to be used for difficult applications, in gasiform environments and represent one of the mobile robot’s sensor options. Our purpose of this study lies in presenting a method of detecting the distance between the mobile robot and obstacles and recording it, and in the same time the possibility of implementing it in order for it to be used by individuals (subjects) in applied studies.

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“…We have used these data sets to explore the behavior of features generated from the signal data of classes of outdoor objects as a step toward designing multisensor classification algorithms that afford mobile robots the ability to characterize outdoor objects. The research presented here is an extension of our previous work involving sonar sensor interpretation by mobile robots (Hinders, Gao, & Fehlman, 2007). This research focused initially on the design of algorithms to distinguish common outdoor objects such as trees, poles, fences, walls, and hedges based on features generated from backscattered sonar echoes.…”

Section: Introductionmentioning

confidence: 99%

Passive infrared thermographic imaging for mobile robot object identification

Fehlman

Hinders

2009

Journal of Field Robotics

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The usefulness of thermal infrared imaging as a mobile robot sensing modality is explored, and a set of thermalphysical features used to characterize passive thermal objects in outdoor environments is described. Objects that extend laterally beyond the thermal camera's field of view, such as brick walls, hedges, picket fences, and wood walls, as well as compact objects that are laterally within the thermal camera's field of view, such as metal poles and tree trunks, are considered. Classification of passive thermal objects is a subtle process because they are not a source for their own emission of thermal energy. A detailed analysis is included of the acquisition and preprocessing of thermal images, as well as the generation and selection of thermal-physical features from these objects within thermal images. Classification performance using these features is discussed, as a precursor to the design of a physics-based model to automatically classify these objects. Published

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Sonar Sensor Interpretation and Infrared Image Fusion for Mobile Robotics

Cited by 7 publications

References 22 publications

Application of matrix multiplication in signal sensor image perception

Application of matrix multiplication in signal sensor image perception

Object Detection with Ultrasound Sensors in Mobile Robots Working Area

Passive infrared thermographic imaging for mobile robot object identification

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