<div style="text-align:justify;"> In this work, a design of a compact optical MEMS-based lidar scanning system with a large field of view (FOV) and small distortion is presented. The scanning system ap...<div style="text-align:justify;"> In this work, a design of a compact optical MEMS-based lidar scanning system with a large field of view (FOV) and small distortion is presented. The scanning system applies an off-axis structure and the length of the system can be reduced to about 10 cm in an optimized way. Simulation results show that a large FOV is achieved under a uniform scanning scheme. In addition, the spot size less than 20 cm at distance of 100 m is also realized. The optical scanning system can be used for the vehicle-mounted Lidar. </div>展开更多
Agricultural projects in different parts of the world depend on underground water wells.Recently,there have been many unfortunate incidents inwhich children have died in abandoned undergroundwells.Providing topographi...Agricultural projects in different parts of the world depend on underground water wells.Recently,there have been many unfortunate incidents inwhich children have died in abandoned undergroundwells.Providing topographical information for these wells is a prerequisite to protecting people from the dangers of falling into them,especially since most of these wells become buried over time.Many solutions have been developed recently,most with the aimof exploring these well areas.However,these systems suffer fromseveral limitations,including high complexity,large size,or inefficiency.This paper focuses on the development of a smart exploration unit that is able to investigate underground well areas,build a 3D map,search for persons and animals,and determine the levels of oxygen and other gases.The exploration unit has been implemented and validated through several experiments using various experiment testbeds.The results proved the efficiency of the developed exploration unit,in terms of 3D modeling,searching,communication,and measuring the level of oxygen.The average accuracy of the 3D modeling function is approximately 95.5%.A benchmark has been presented for comparing our results with related works,and the comparison has proven the contributions and novelty of the proposed system’s results.展开更多
Atmospheric turbulence is an important parameter affecting laser atmospheric transmission.This paper reports on a self-developed atmospheric turbulence detection Li DAR system(scanning differential image motion Li DAR...Atmospheric turbulence is an important parameter affecting laser atmospheric transmission.This paper reports on a self-developed atmospheric turbulence detection Li DAR system(scanning differential image motion Li DAR(DIM-Li DAR)system).By designing and simulating the optical system of atmospheric turbulence detection Li DAR,the basic optical imaging accuracy has been determined.展开更多
Light detection and ranging(LiDAR)is widely used for active three-dimensional(3D)perception.Beam scanning LiDAR provides high accuracy and long detection range with limited detection efficiency,while flash LiDAR can a...Light detection and ranging(LiDAR)is widely used for active three-dimensional(3D)perception.Beam scanning LiDAR provides high accuracy and long detection range with limited detection efficiency,while flash LiDAR can achieve high-efficiency detection through the snapshot approach at the expense of reduced accuracy and range.With the synergy of these distinct detection approaches,we develop a miniaturized dual-mode,reconfigurable beam forming device by cascading Pancharatnam-Berry phase and propagation phase metasurfaces,integrated with a microactuator.By modulating incident light polarization,we can switch the output beam of the device between the beam array scanning mode and flash illuminating mode.In the scanning mode,the device demonstrates a continuously tunable angular resolution and a±35°field of view(FoV)through driving the micro-actuator to achieve the lateral translation of±100μm.In the flash mode,uniform illumination across the entire FoV is achieved.As a proof of concept,we propose an adaptive 3D reconstruction scheme that leverages the device’s capability to switch operation modes and adjust detection resolution.Together,the proposed device and the detection scheme constitute a dualmode LiDAR system,demonstrating high adaptability to diverse environments and catalyze the applications of more efficient and compact 3D detection systems.展开更多
As mining operations extend to greater depths, the risk of deformation in high-stress tunnels increases significantly, posing a substan-tial threat. This study introduces a novel framework known as“robust mobility de...As mining operations extend to greater depths, the risk of deformation in high-stress tunnels increases significantly, posing a substan-tial threat. This study introduces a novel framework known as“robust mobility deformation detection” (RM2D), designed for real-timetunnel deformation detection. RM2D employs mobile LiDAR scanner to capture real-time point cloud data within the tunnel. This datais then voxelized and analyzed using covariance matrices to create a voxel-based multi-distribution representation of the rugged tunnelsurface. Leveraging this representation, we assess deformations and scrutinize results through machine learning models to swiftly pin-point tunnel deformation locations. Extensive experimental validation confirms the framework’s capacity to successfully detect deforma-tions, including floor heave, side rib spalling, and roof fall, with remarkable accuracy. For deformation levels at 0.15 m, RM2D was ableto successfully detect deformations with an area greater than 2 m^(2) . For deformation areas of (3 ± 0.5) m^(2) , RM2D successfully detecteddeformations of levels at (0.05 ± 0.01) m, and its detection capability meets the standard criteria for mining tunnel deformation detec-tion. When compared to two conventional methods, RM2D demonstrates its real-time deformation detection capability in complex envi-ronments and on rough surfaces with precision, all at speeds below 10 km/h. Furthermore, we evaluated the predictive performance usingmultiple evaluation metrics and provided insights into the decision mechanism of the machine learning employed in our research, therebyoffering valuable information for practical engineering applications in tunnel deformation detection.展开更多
文摘<div style="text-align:justify;"> In this work, a design of a compact optical MEMS-based lidar scanning system with a large field of view (FOV) and small distortion is presented. The scanning system applies an off-axis structure and the length of the system can be reduced to about 10 cm in an optimized way. Simulation results show that a large FOV is achieved under a uniform scanning scheme. In addition, the spot size less than 20 cm at distance of 100 m is also realized. The optical scanning system can be used for the vehicle-mounted Lidar. </div>
基金financially supported by the Deanship of Scientific Research(DSR)at the University of Tabuk,Tabuk,Saudi Arabia,under Grant No.[1441-105].
文摘Agricultural projects in different parts of the world depend on underground water wells.Recently,there have been many unfortunate incidents inwhich children have died in abandoned undergroundwells.Providing topographical information for these wells is a prerequisite to protecting people from the dangers of falling into them,especially since most of these wells become buried over time.Many solutions have been developed recently,most with the aimof exploring these well areas.However,these systems suffer fromseveral limitations,including high complexity,large size,or inefficiency.This paper focuses on the development of a smart exploration unit that is able to investigate underground well areas,build a 3D map,search for persons and animals,and determine the levels of oxygen and other gases.The exploration unit has been implemented and validated through several experiments using various experiment testbeds.The results proved the efficiency of the developed exploration unit,in terms of 3D modeling,searching,communication,and measuring the level of oxygen.The average accuracy of the 3D modeling function is approximately 95.5%.A benchmark has been presented for comparing our results with related works,and the comparison has proven the contributions and novelty of the proposed system’s results.
基金jointly funded by the National Science Foundation of China(No.42405069)the University Natural Sciences Research Project of Anhui Province(Nos.2023AH052201 and 2023AH052184)+1 种基金the 2023 Talent Research Fund Project of Hefei University(No.23RC01)the Technical Development Project of Hefei University(Nos.902/22050124128,902/22050124148 and 902/22050124250)。
文摘Atmospheric turbulence is an important parameter affecting laser atmospheric transmission.This paper reports on a self-developed atmospheric turbulence detection Li DAR system(scanning differential image motion Li DAR(DIM-Li DAR)system).By designing and simulating the optical system of atmospheric turbulence detection Li DAR,the basic optical imaging accuracy has been determined.
基金supported by National Natural Science Foundation of China(Grant No.U21A6003)National Key Research and Development Program of China(Grant No.2023YFB3906300)+1 种基金support from the Beijing Outstanding Young Scientist Program(Grant No.JWZQ20240101028)support from National Natural Science Foundation of China(Grant No.62475018).
文摘Light detection and ranging(LiDAR)is widely used for active three-dimensional(3D)perception.Beam scanning LiDAR provides high accuracy and long detection range with limited detection efficiency,while flash LiDAR can achieve high-efficiency detection through the snapshot approach at the expense of reduced accuracy and range.With the synergy of these distinct detection approaches,we develop a miniaturized dual-mode,reconfigurable beam forming device by cascading Pancharatnam-Berry phase and propagation phase metasurfaces,integrated with a microactuator.By modulating incident light polarization,we can switch the output beam of the device between the beam array scanning mode and flash illuminating mode.In the scanning mode,the device demonstrates a continuously tunable angular resolution and a±35°field of view(FoV)through driving the micro-actuator to achieve the lateral translation of±100μm.In the flash mode,uniform illumination across the entire FoV is achieved.As a proof of concept,we propose an adaptive 3D reconstruction scheme that leverages the device’s capability to switch operation modes and adjust detection resolution.Together,the proposed device and the detection scheme constitute a dualmode LiDAR system,demonstrating high adaptability to diverse environments and catalyze the applications of more efficient and compact 3D detection systems.
基金supported in part by the National Key Research and Development Program of China(Grant No.2023YFC2907305).
文摘As mining operations extend to greater depths, the risk of deformation in high-stress tunnels increases significantly, posing a substan-tial threat. This study introduces a novel framework known as“robust mobility deformation detection” (RM2D), designed for real-timetunnel deformation detection. RM2D employs mobile LiDAR scanner to capture real-time point cloud data within the tunnel. This datais then voxelized and analyzed using covariance matrices to create a voxel-based multi-distribution representation of the rugged tunnelsurface. Leveraging this representation, we assess deformations and scrutinize results through machine learning models to swiftly pin-point tunnel deformation locations. Extensive experimental validation confirms the framework’s capacity to successfully detect deforma-tions, including floor heave, side rib spalling, and roof fall, with remarkable accuracy. For deformation levels at 0.15 m, RM2D was ableto successfully detect deformations with an area greater than 2 m^(2) . For deformation areas of (3 ± 0.5) m^(2) , RM2D successfully detecteddeformations of levels at (0.05 ± 0.01) m, and its detection capability meets the standard criteria for mining tunnel deformation detec-tion. When compared to two conventional methods, RM2D demonstrates its real-time deformation detection capability in complex envi-ronments and on rough surfaces with precision, all at speeds below 10 km/h. Furthermore, we evaluated the predictive performance usingmultiple evaluation metrics and provided insights into the decision mechanism of the machine learning employed in our research, therebyoffering valuable information for practical engineering applications in tunnel deformation detection.
