Micro-distortion detection of lidar scanning signals based on geometric analysis

Liu, Shuai ORCID: https://orcid.org/0000-0001-9909-0664, Chen, Xiang, Li, Ying and Cheng, Xiaochun ORCID: https://orcid.org/0000-0003-0371-9646 (2019) Micro-distortion detection of lidar scanning signals based on geometric analysis. Symmetry, 11 (12) , 1471. pp. 2-13. ISSN 2073-8994 [Article] (doi:10.3390/sym11121471)

PDF - Published version (with publisher's formatting) Available under License Creative Commons Attribution 4.0. Download (2MB)

Abstract

When detecting micro-distortion of lidar scanning signals, current hardwires and algorithms have low compatibility, resulting in slow detection speed, high energy consumption, and poor performance against interference. A geometric statistics-based micro-distortion detection technology for lidar scanning signals was proposed. The proposed method built the overall framework of the technology, used TCD1209DG (made by TOSHIBA, Tokyo, Japan) to implement a linear array CCD (charge-coupled device) module for photoelectric conversion, signal charge storage, and transfer. Chip FPGA was used as the core component of the signal processing module for signal preprocessing of TCD1209DG output. Signal transmission units were designed with chip C8051, FT232, and RS-485 to perform lossless signal transmission between the host and any slave. The signal distortion feature matching algorithm based on geometric statistics was adopted. Micro-distortion detection of lidar scanning signals was achieved by extracting, counting, and matching the distorted signals. The correction of distorted signals was implemented with the proposed method. Experimental results showed that the proposed method had faster detection speed, lower detection energy consumption, and stronger anti-interference ability, which effectively improved micro-distortion correction.

Actions (login required)

View Item

Statistics

DownloadsShow export options

Export as CSVXMLJSON

Activity Overview

6 month trend

108Downloads

6 month trend

149Hits

Additional statistics are available via IRStats2.