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location estimation of autonomous driving robot and 3D tunnel mapping in underground mines using pattern matched LiDAR sequential images

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International Journal of Mining Science and Technology 2021年第5期31卷 779-788页

作者： Heonmoo Kim Yosoon Choi Department of Energy Resources Engineering Pukyong National UniversityBusan 48513Republic of Korea

In this study,a machine vision-based pattern matching technique was applied to estimate the location of an autonomous driving robot and perform 3D tunnel mapping in an underground mine *** autonomous driving robot continuously detects the wall of the tunnel in the horizontal direction using the light detection and ranging(Li DAR)sensor and performs pattern matching by recognizing the shape of the tunnel *** proposed method was designed to measure the heading of the robot by fusion with the inertial measurement units sensor according to the pattern matching accuracy;it is combined with the encoder sensor to estimate the location of the *** addition,when the robot is driving,the vertical direction of the underground mine is scanned through the vertical Li DAR sensor and stacked to create a 3D map of the underground *** performance of the proposed method was superior to that of previous studies;the mean absolute error achieved was 0.08 m for the X-Y axes.A root mean square error of 0.05 m^(2)was achieved by comparing the tunnel section maps that were created by the autonomous driving robot to those of manual surveying.

关键词： Pattern matching location estimation Autonomous driving robot 3D tunnel mapping Underground mine

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A data-driven method for estimating the target position of low-frequency sound sources in shallow seas

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Frontiers of Information Technology & Electronic Engineering 2021年第7期22卷 1020-1030页

作者： Xianbin SUN Xinming JIA Yi ZHENG Zhen WANG School of Mechanical and Automobile Engineering Qingdao University of TechnologyQingdao 266000China Institute of Oceanographic Instrumentation Shandong Academy of SciencesQingdao 266000China

Estimating the target position of low-frequency sound sources in a shallow sea environment is difficult due to the high cost of hydrophone placement and the complexity of the propagation *** propose a compressed recurrent neural network(C-RNN)model that compresses the signal received by a vector hydrophone into a dynamic sound intensity signal and compresses the target position of the sound source into a GeoHash *** types of data are used to carry out prior training on the recurrent neural network,and the trained network is subsequently used to estimate the target position of the sound *** with traditional mathematical models,the C-RNN model functions independently under the complex sound field environment and terrain conditions,and allows for real-time positioning of the sound source under low-parameter operating *** results show that the average error of the model is 56 m for estimating the target position of a low-frequency sound source in a shallow sea environment.

关键词： Vector hydrophone Shallow sea Low frequency location estimation Recurrent neural network

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Target Localization Based on Angle of Arrivals

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Journal of Electronic Science and Technology of China 2007年第2期5卷 172-174页

作者： Yi-Chao Cao College of Information Science and Technology Donghua University Shanghai 201600 China

Mobile location using angle of arrival （AOA） measurements has received considerable attention. This paper presents an approximation of maximum likelihood estimator （MLE） for localizing a source based on AOA measurements. By introducing an intermediate variable, the nonlinear equations relating AOA estimates can be transformed into a set of equations which are linear in the unknown parameters. It is an approximate realization of the MLE. Simulations show that the proposed algorithm outperforms the previous contribution.

关键词： Angle of arrival (AOA) direction-of-arrival estimation location estimation Monte Carlo methods wireless localization.

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