Strict self-checking algorithm of domestic lidar system based on virtual conjugate point

Abstract

The invention discloses a rigorous self-calibration algorithm of a domestic laser radar system based on a virtual conjugate point. The rigorous self-calibration algorithm comprises steps of extracting a corresponding virtual conjugate point and obtaining position posture information corresponding to the point in order to obtain virtual conjugate points data, introducing the virtual conjugate point data into a self-calibration model, introducing a gauss markov model based on the least square, examining whether the data is convergent in an iteration calculation process, if yes, obtaining optimal 8 calibration parameters, substituting the 8 calibration parameters into initial data to re-calculate laser point cloud data, obtaining the high precision laser point cloud data after calibration to perform accuracy evaluation and outputting an accuracy report. The invention improves positioning precision of the laser radar system while improving the system calibration accuracy, which assists to generate the seamless splicing air strip data and has and has a very important meaning to the post-processing of the laser radar data and the application.

Description

technical field [0001] The invention relates to the field of laser radar, in particular to a strict self-checking algorithm of a domestic laser radar system based on a virtual conjugate point. Background technique [0002] As a new type of active multi-sensor integrated system, the LiDAR system has many advantages and can quickly obtain high-resolution digital terrain models, and has unique advantages in the fields of terrain mapping, forest survey, and disaster monitoring. In the positioning process of the LiDAR system, the system error will cause the offset and dislocation of the overlapping flight belt data, which will bring a lot of inconvenience to the post-processing of the data. Therefore, reducing the system error and improving the data quality and accuracy are very important for LiDAR post-processing. significance. [0003] There are currently two types of methods for lidar system calibration and elimination of system errors. The first method: based on strip data,...

AI Technical Summary

Problems solved by technology

[0005] 1. The algorithm assumes many conditions in the calculation process, which conflicts with the actual situation of flight and data in the actual application, and lacks practicability; 2. Some calibration methods are time-consuming and expensive, especially the route calibration. It is necessary to strictly carry out route design and flight to meet the calibration requirements; 3. The calibration steps are complex, and some are even step-by-step calibration, and the calibration process for the three angles is in order; 4. It is necessary to check the known control points Or the acquisition of reference data such as control surfaces to assist in obtaining calibration parameters; 5. Some calibration methods require manual participation, or a certain amount of experience is involved in order to obtain better calibration accuracy; 6. There is currently no common Calibration methods, because most of the calibration methods are based on manual software packages or expert knowledge of LiDAR data providers

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