Laser sensor depth data reconstruction method based on compressed sampling matching pursuit

Abstract

The invention discloses a laser sensor depth data reconstruction method based on compressed sampling matching pursuit. Data quantity obtained through collection of a laser sensor is high, the collected data is compressed based on screening of massive redundant data carried out by a measurement matrix; and through decompression of the data, plant images can be reconstructed. According to the method, the compressed sampling matching pursuit algorithm is employed. According to the algorithm, atoms iterated at each time may be discarded in a next iteration process; residual errors will be correspondingly updated; and the reconstruction precision is improved. According to the method, the sensor data storage and transmission pressure is greatly reduced by starting with the data collected by the sensor; and the compressed depth data has great significance in stage growth analysis and data query of crops.

Description

technical field [0001] The invention relates to the reconstruction technology of laser sensor depth data, in particular, it is a laser sensor depth data reconstruction method based on compressed sampling matching pursuit. Background technique [0002] With the increasing application of mechanization in agriculture, in the modern production process, the instant grasp of crop growth information and the statistical analysis of crop growth status data are used to control plant diseases and insect pests to achieve high yields of agricultural crops A bumper harvest is of great significance to the development of agricultural economy in my country's national economy. [0003] Traditional crop morphological feature detection is mainly through the technology of real-time shooting video or photos. However, due to the long growth cycle of plants, the video effect is greatly affected by the weather and the change of day and night. This method has a huge impact on the transmission and stor...

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Problems solved by technology

[0003] Traditional crop morphological feature detection is mainly through the technology of real-time shooting video or photos. However, due to the long growth cycle of plants, the video effect is greatly affected by the weather and the change of day and night. This method has a huge impact on the transmission and storage of plant feature data. It is also not conducive to the periodic and effective statistics and analysis of the growth status of the same crop in the future

[0004] The amount of data obtained by laser sensor data collection is generally relatively large, which will bring huge pressure to the process of storage and transmission

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