Micro-distortion detection of lidar scanning signals based on geometric analysis
Article
Liu, S., Chen, X., Li, Y. and Cheng, X. 2019. Micro-distortion detection of lidar scanning signals based on geometric analysis. Symmetry. 11 (12), pp. 2-13. https://doi.org/10.3390/sym11121471
| Type | Article |
|---|---|
| Title | Micro-distortion detection of lidar scanning signals based on geometric analysis |
| Authors | Liu, S., Chen, X., Li, Y. and Cheng, X. |
| 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. |
| Keywords | geometric analysis, lidar scanning signal, micro-distortion, detection technology, TCD1209DG, lossless signal transmission |
| Publisher | MDPI |
| Journal | Symmetry |
| ISSN | 2073-8994 |
| Electronic | 2073-8994 |
| Publication dates | |
| 03 Dec 2019 | |
| Online | 03 Dec 2019 |
| Publication process dates | |
| Deposited | 06 Dec 2019 |
| Accepted | 29 Nov 2019 |
| Output status | Published |
| Publisher's version | License |
| Copyright Statement | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/sym11121471 |
| Language | English |
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