Position and posture estimation method of driverless car based on distance from point to surface and cross correlation entropy rectification

Abstract

The invention discloses a position and posture estimation method of a driverless car based on a distance from a point to a surface and cross correlation entropy rectification, which comprises the following steps: firstly, a three-dimensional laser radar is calibrated, and then the acquired three-dimensional laser radar data is subjected to coordinate conversion; point cloud alignment is carried out on the acquired data and the existing map data to obtain a rotary and translation transformation of a rigid body; so that the position and posture of an active moving body are obtained according tothe rotary and translation conversion. According to the invention, by using the three-dimensional laser radar as the data source, the function of estimating the position and posture of the driverlesscar is finished through the steps of coordinate system conversion, data drop sampling, point set rectification and the like. The method can well overcome the influence of weather, light and other environmental factors. Moreover, the error evaluation function based on the distance from the point to the surface and the cross correlation entropy has good resistance to noise and abnormal points, suchas mismatching of the scene and the map description part, dynamic obstacles and the like, therefore, the function of accurate and robust estimation of the position and posture of the driverless car can be achieved.

Description

technical field [0001] The invention belongs to the field of positioning and navigation in unmanned vehicle technology, in particular to an unmanned vehicle pose estimation method based on point-to-plane distance and cross-correlation entropy registration. Background technique [0002] In recent years, with the rapid development of the automobile industry, traffic accidents have become a global problem. It is estimated that the number of deaths and injuries in traffic accidents in the world exceeds more than 500,000 people every year. Born for human driving applications. Unmanned vehicle positioning technology is one of the key components of unmanned driving technology. In order to obtain the current location of the driverless car in real time, the driverless car is equipped with various active and passive sensors, including cameras, lidar, GPS and IMU. In the early research, the method of using GPS for positioning has been widely used. However, the use of GPS is greatly ...

AI Technical Summary

Problems solved by technology

However, one of the most important shortcomings of visual technology is the relatively strict requirements for the external environment.

For situations such as shadows in the environment, complex driving environment, missing roadside markings (or signs), poor lighting, low visibility, or bad weather, the image information obtained by vision technology (camera) often has a low signal-to-noise ratio. The accuracy of the algorithm brings great challenges, and the positioning accuracy of the system cannot be guaranteed

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