Multi-source sensor joint calibration method based on three-dimensional regular octagonal structure

Abstract

The invention discloses a multi-source sensor joint calibration method based on a three-dimensional regular octagonal structure. The method comprises the steps of calibrating internal parameters of acamera; constructing a three-dimensional laser point cloud model; correcting the three-dimensional laser point cloud based on inertial data; optimizing a laser jitter error superposition mean value; optimizing the laser stepping angle error increment; and constructing a multi-line-segment joint external parameter calibration model, and solving an external parameter optimal solution through a nonlinear method. The method can effectively improve the joint calibration precision and the data fusion effect of the multi-source sensor in a complex agriculture and forestry environment.

Description

technical field [0001] The invention relates to the field of multi-sensor data fusion, in particular to a joint calibration method for multi-source data in complex agricultural and forestry environments. Background technique [0002] In the development of modern agriculture and forestry, the trend of joint application of multi-source data is very obvious. Compared with visible light imaging, the laser scanning field of view is relatively large, but the point cloud density is relatively sparse, and there is more noise; while the thermal infrared imager can image in dark conditions, but the field of view is relatively small, and can only display In the small area of ​​the whole scene, the comprehensive use of these different source data can learn from each other and improve the scanning detection ability and target segmentation and recognition ability of agricultural and forestry mobile devices. But this requires multi-sensor data preprocessing and coordinate joint calibrat...

AI Technical Summary

Problems solved by technology

However, these methods have many shortcomings and are not suitable for multi-sensor calibration applications in complex agricultural and forestry environments:

[0004] (1) The steps of the joint external calibration process and the camera calibration process are separated, and the number of calibration reference points available for a single calibration process is too small, requiring multiple trials to obtain sufficient calibration data, which is inefficient;

[0005] (2) The three-dimensional laser data is affected by the internal characteristics of the instrument and the reflectivity characteristics of the object, resulting in measurement errors in the actual measured distance value of each laser point cloud;

[0006] (3) Due to the influence of the spectroscopic motor, the 3D laser instrument obtains relatively sparse point cloud data. When the edge of the object is measured, the halo effect is prone to occur, resulting in a step angle error;

[0007] (4) The calibration reference point is the edge point of an independent object, which requires precise manual extraction and information input process, increases the probability of human interference error, and is not suitable for scenes with complex environments and large distance differences between objects

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