Research of Multi-Sensor Data Fusion Based on Binocular Vision Sensor and Laser Range Sensor

Wang, Qi Long; Li, Jian Yong; Shen, Hai; Song, Teng; Xuan, Yan

doi:10.4028/www.scientific.net/kem.693.1397

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…The analysis is based on the structure of the proposed method and gives the design of the hardware structure and software system. [16][17][18][19][20] The local manifold structure of the sample is analyzed using the abnormal behavior detection method, which is based on LLE and sparse description. That improves the efficiency and accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…The mean shift algorithm provides non-parametric prediction using density gradient. It provides efficient iteration via optimization of the probability density function of the pixel feature point when tracks the moving target frame and can obtain the local maximum of the density function, 16 complete high-efficiency orientation of the moving target, and adapt to the complex motions of the stage performance target such as deformation and rotation.…”

Section: Target Tracking Of the Stage Performance Based On The Mean Shift Algorithmmentioning

confidence: 99%

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An effective method for the abnormal monitoring of stage performance based on visual sensor network

Zhang

Ding

et al. 2018

International Journal of Distributed Sensor Networks

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Abnormal monitoring of stage performance plays a vital role in the stage performance. For the real-time stage performance, detection efficiency and accuracy are particularly important. As the traditional monitoring method based on sparse description model to realize abnormal behavior of stage performance did not realize the manifold structure during the performance, the behavior characteristics are sparse, and the decomposition has higher volatility, the recognition accuracy of abnormal behavior is low. Therefore, an abnormal monitoring method of stage performance based on visual sensor network is proposed, the overall structure of the abnormal monitoring system of stage performance based on the vision sensor network is analyzed, the hardware structure and software composition of the system are designed, and the method of monitoring the abnormal behavior of the system is analyzed emphatically. Through the background subtraction, the weighted threshold-based segmentation of the target image from the background image, the chaotic search particle swarm optimization algorithm based on image target detection and tracking algorithm for target tracking by mean shift, the abnormal behavior of local linear embedding and detection method based on sparse representation, a comprehensive analysis of the local manifold structure of sample is set. Enhance the stage performance of abnormal behavior detection efficiency and accuracy. The experimental results show that the proposed method has higher detection efficiency and accuracy and has higher robustness.

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Section: Discussionmentioning

confidence: 99%

Section: Target Tracking Of the Stage Performance Based On The Mean Shift Algorithmmentioning

confidence: 99%

An effective method for the abnormal monitoring of stage performance based on visual sensor network

Zhang

Ding

et al. 2018

International Journal of Distributed Sensor Networks

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“…The main concept of this technique is to obtain the whole area information to generate an exhaustive cost-to-go function for planning. This is usually done by using laser range sensor data [11,[104][105][106][107][108]. The area is typically discretised into square grid cells, as it is converted to a grid-base map.…”

Section: Metric Path Planningmentioning

confidence: 99%

Path planning for autonomous bulldozers

et al. 2019

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With increasing demand and awareness from industries for enhanced safety, higher productivity, and better vehicle utilization, coupled with labour shortage, automating heavy equipment is becoming more and more important. Among the different types of heavy equipment, bulldozers fall into the category of machines that have a very high potential of achieving fully autonomous operation. Focusing on this fact, the aim of this research is to develop an automated path planning system for bulldozers. In general, an autonomous system is composed of terrain mapping, path planning and path-tracking. Existing commercial grade methodologies already solve terrain mapping and path-tracking, however path planning will be a key challenge for bulldozer automation.Conventional path planning algorithms are designed typically using a grid-based map, and are very versatile. However, in reality, a substantial effort is still required when applying to specific industry products. In contrast to this trend, the aim of this work is to develop a path planning algorithm specifically suitable for bulldozers, starting from the phase of theoretical development. The novel path planning methodology was developed by incorporating industry feedback, and has successfully resolved the issues which occurred when attempting to apply the pre-existing path planning methodologies. Results obtained from this newly developed methodology will demonstrate that efficient paths can be created. The methodology presented in this thesis has led to the development of automated path planning for bulldozers. This allows bulldozers to function without the need of an operator, contributing to enhanced safety, increased productivity, and solving the shortage of skilled operators.Declaration relating to disposition of project thesis/dissertation I hereby grant to the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all property rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation.I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstracts International (this is applicable to doctoral theses only). …………………………………………………………… Signature ……………………………………..……………… Witness Signature ……….……………………...…….… DateThe University recognises that there may be exceptional circumstances requiring restrictions on copying or conditions on use. Requests for restriction for a period of up to 2 years must be made in writing. Requests for a longer period of restriction may be considered in exceptional circumstances and require the approval of the Dean of Graduate Research.

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Geodetic coordinate calculation based on monocular vision on UAV platform

Yang

et al. 2016

2016 IEEE 13th International Conference on Signal Processing (ICSP)

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Research of Multi-Sensor Data Fusion Based on Binocular Vision Sensor and Laser Range Sensor

Cited by 7 publications

References 2 publications

An effective method for the abnormal monitoring of stage performance based on visual sensor network

An effective method for the abnormal monitoring of stage performance based on visual sensor network

Path planning for autonomous bulldozers

Geodetic coordinate calculation based on monocular vision on UAV platform

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