Airborne laser point cloud data and vehicle laser point cloud data fusion method

Abstract

The invention discloses an airborne laser point cloud data and vehicle laser point cloud data fusion method, comprising: employing airborne data as reference data to extract homonymy points or same characteristic lines of vehicle laser point cloud data; matching the vehicle laser point cloud data and airborne laser point cloud data in a vehicle laser point cloud data coordinate system; performing thinning treatment on the vehicle laser point cloud data in the coordinate system, and removing superfluous vehicle laser point cloud data; classifying the vehicle laser point cloud data in the coordinate system according to ground object types, and obtaining vehicle laser point cloud data of different types; and deleting the overlapped section between the airborne laser point cloud data and the vehicle laser point cloud data under the same category in the coordinate system, maintaining the point cloud data with relatively high data density in the overlapped section under the same category, and obtaining fused point cloud data. The scheme overcomes the difference of resolution ratio and data size between two types of data, and can well fuse the two types of data.

Description

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AI Technical Summary

Benefits of technology

This patented technology collects both aerially collected laser points from vehicles or ground based devices (VL) on different areas without any differences between them. It then uses this gathered data to accurately correct errors caused during the process. By doing these operations efficiently, it allows for effective use of space resources while also improving the quality of the final resultant image.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving the efficiency at which vehicles use aerial or vehicular laser systems to gather accurate geographic location (latitude) values from their surrounding areas during urban planning operations. Current methods such as combining different types of sensors like cameras and radars together may result in poor alignment due to differences between sensor readings caused by factors like atmospheric conditions and signal interference.

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