Laser radar and camera combined calibration method based on mobile calibration plate

Abstract

The invention discloses a laser radar and camera combined calibration method based on a mobile calibration plate. Firstly, calibration internal parameters are calculated for different poses of a calibration plate; a normal vector of a point cloud data fitting plane of the selected corresponding laser radar calibration plate is acquired, and calibration external parameters are calculated through equation solving to finally complete calibration. The method comprises the following specific steps: step 1, collecting laser radar and camera data of calibration plates at different angles in the viewfield of a camera as calibration data and verification data; 2, acquiring a camera internal reference calibration result of the acquired camera data by using a Zhang Zhengyou calibration method; 3, selecting a calibration plate position point cloud on the corresponding laser radar data; step 4, carrying out plane fitting on the point cloud data obtained in the step 3 and obtaining a correspondingnormal vector; and step 5, substituting the internal reference data obtained in the step 2 into a corresponding calculation formula to obtain a final external reference result. Compared with a single-point selection method, the method for selecting the plane data of the calibration plate is higher in accuracy and simpler to operate.

Description

technical field [0001] The invention belongs to the field of intelligent vehicle perception, and specifically refers to a method for joint calibration of a laser radar and a camera based on a mobile calibration board. Background technique [0002] A smart car is a complex system including perception, planning, decision-making and control. Perception is the basis of other parts. The richer the perception information is, the more beneficial it is for the subsequent subsystems. At present, a single sensor is difficult to meet the complex surrounding perception requirements of smart cars. Multi-sensor fusion algorithms have become the current mainstream algorithms, and the basis of fusion algorithms is the joint calibration between sensors. [0003] Joint calibration can be divided into two parts: internal reference calibration and external reference calibration. Internal reference calibration is to determine the internal mapping relationship of the sensor, mainly including cam...

AI Technical Summary

Problems solved by technology

At present, it is difficult for a single sensor to meet the complex surrounding perception requirements of smart cars. Multi-sensor fusion algorithms have become the current mainstream algorithms, and the basis of fusion algorithms is the joint calibration between sensors.

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