公开(公告)号：US20240280707A1

公开(公告)日：2024-08-22

申请号：US18434754

申请日：2024-02-06

Applicant: GUILIN UNIVERSITY OF TECHNOLOGY

Inventor： Guoqing ZHOU ,  Jianwu LIU ,  Jiasheng XU ,  Ertao GAO ,  Xinkun YUAN

IPC: G01S19/37 ,  G01S19/25 ,  G01S19/53

CPC classification number: G01S19/37 ,  G01S19/256 ,  G01S19/53

Abstract: The present invention discloses a POS high-accuracy positioning method under occlusion, comprising: integrating single-point positioning data and differential correction data for solution to acquire high-accuracy position information; implementing a buffer through an RAM of an FPGA to cache the collected position information and a sampling time; when a satellite signal loss is detected by a system, fetching five pieces of position information from the RAM at a time before the signal is lost and five pieces of position information at a time after the signal is reappeared, and filtering the information to obtain filtered data; constructing an equation by using the filtered data, and estimating a signal lost position. By triggering an occlusion algorithm through the FPGA, the present invention improves an ability of a POS system to work in areas with weak satellite signals and meanwhile reduces an operation time, thereby meeting practical needs.

公开(公告)号：US20240183952A1

公开(公告)日：2024-06-06

申请号：US18527403

申请日：2023-12-04

Applicant: GUILIN UNIVERSITY OF TECHNOLOGY

Inventor： Guoqing ZHOU ,  Haotian ZHANG

IPC: G01S7/4861 ,  G01S7/00 ,  G01S7/484 ,  G01S17/89

CPC classification number: G01S7/4861 ,  G01S7/003 ,  G01S7/484 ,  G01S17/89

Abstract: The present disclosure relates to a lightweight and small bathymetry LiDAR multi-channel high sampling rate high-precision real-time synchronous acquisition and storage system, which adopts an ADC+FPGA+ZYNQ architecture to implement four-channel high-rate real-time synchronous parallel sampling, has synchronization error less than 300 ps, sampling rate s as high as 2 GSPS, and sampling precision as high as 14 bits, and comprises an FPGA system acquisition carrier board unit for implementing laser radar echo data acquisition and storage, PMT controlling, data maximum value feedback, peripheral interface design and storage control; a storage daughter board unit for implementing storage of echo data and export of 100 Mbps Ethernet, and an upper computer data conversion software for implementing conversion of original echo data files into decimal or hexadecimal csv files. The system has multi-channel parallel acquisition, high sampling rate and precision, strong real-time performance and functional applicability, and light weight and portability for bathymetry LiDAR.

公开(公告)号：US20210396853A1

公开(公告)日：2021-12-23

申请号：US17345799

申请日：2021-06-11

Applicant: Guilin University of Technology

Inventor： Guoqing ZHOU ,  Xiang ZHOU ,  Jiasheng XU

IPC: G01S7/481 ,  G01S17/894 ,  G01S17/933 ,  G01S17/32

Abstract: An airborne super-continuum 50-band hyperspectral light detection and ranging system comprises an integrated control system, a storage unit, a super-continuum laser system, an optical transmitting system, a reflecting mirror, a scanning system, an optical receiving system, a super-continuum hyperspectral laser detection system, a plane array CCD camera. The operation process includes super-continuum laser system emitting continuous hyperspectral pulsed lasers, performing lasers beam expansion and collimation, emitting it to ground objects, reflecting it, receiving it by the scanning system, transmitting to the optical receiving system, and focusing it into hyperspectral laser detection system for outputting laser hyperspectrum and 3D spatial data, storing laser data in the storage unit with high-resolution multi-spectral data. The system acquires hyperspectral spectrum data with 50 bands at aspectral range of 400-900 nm, spectral resolution of 10 nm and 3D spatial data of ground objects with a ground resolution better than 0.5 meters.

公开(公告)号：US20240418832A1

公开(公告)日：2024-12-19

申请号：US18557926

申请日：2023-06-21

Applicant: GUILIN UNIVERSITY OF TECHNOLOGY

Inventor： Guoqing ZHOU ,  Zhexian LIU

IPC: G01S7/481 ,  G02B25/00

Abstract: The present disclosure relates to a stray light suppression device for bathymetric LiDAR onboard unmanned shipborne, belonging to the technical field of LiDAR water depth detection, particularly to a stray light suppression device for bathymetric LiDAR onboard unmanned shipborne. The device comprises an optical system component, a first objective lens barrel, a second objective lens barrel, a spectroscope barrel, a first eyepiece barrel a, a first eyepiece barrel b, a second eyepiece barrel a, a second eyepiece barrel b, a PMT detector a, a PMT detector b and a spectroscope supporting structure. Stray light suppression of an optical system with minimal field-of-view is realized, and stray light propagated by first-order, second-order and third-order scattered light paths is suppressed, so that a water depth detection capability of the bathymetric LiDAR onboard unmanned shipborne is improved and a dynamic detection range is expanded.

公开(公告)号：US20240020970A1

公开(公告)日：2024-01-18

申请号：US18352246

申请日：2023-07-14

Applicant: GUILIN UNIVERSITY OF TECHNOLOGY

Inventor： Guoqing ZHOU ,  Yue JIANG ,  Haoyu WANG

IPC: G06V20/10 ,  G06T7/11 ,  G06V10/80 ,  G06V10/94 ,  G06V10/764 ,  G06V10/77

CPC classification number: G06V20/176 ,  G06T7/11 ,  G06V10/806 ,  G06V10/95 ,  G06V10/764 ,  G06V10/7715 ,  G06T2207/10028 ,  G06T2207/30181 ,  G06T2207/20081

Abstract: The present disclosure relates to a PointEFF method for urban object classification with LiDAR point cloud data, and belongs to the field of LiDAR point cloud classification. The method comprises: point cloud data segmentation; End-to-end feature extraction layer construction; External feature fusion layer construction; and precision evaluation. The PointEFF method for urban object classification with LiDAR point cloud data fuses point cloud hand-crafted descriptors with End-to-end features obtained from a network at an up-sampling stage of a model by constructing an External Feature Fusion module, which improves a problem of local point cloud information loss caused by interpolation operation in the up-sampling process of domain feature pooling methods represented by PointNet and PointNet++, greatly improves classification precision of the model in complex ground features, especially in rough surface ground features, and is capable of being better applied to the classification of urban ground features with complex ground feature types.