Onboard laser point cloud filtering method based on cylindrical coordinates

Abstract

The invention discloses an onboard laser point cloud filtering method based on cylindrical coordinates. The method comprises: firstly converting onboard laser point cloud from a Cartesian coordinate system (x, y, z) into a cylindrical coordinate system ([rho] ,[phi], d); then, in the cylindrical coordinate system, subjecting the onboard laser point cloud to grid transformation in the plane of [phi]d to form regular grids; and finally subjecting the regular grids to single-section and multi-section filtering based on distance constraint and angle constraint to obtain a final ground point. The method can solve the large point density, the uneven distribution, and a large number of occlusion areas of the onboard laser point cloud, can filter out a large number of large-scale non-ground objects in the onboard laser point cloud, and can maintain the small-scale terrain details in the onboard laser point cloud.

Description

technical field [0001] The invention belongs to the technical field of laser radar remote sensing surveying and mapping, and relates to a vehicle-mounted laser point cloud filtering method, in particular to a vehicle-mounted laser point cloud filtering method based on cylindrical coordinates. Background technique [0002] The vehicle-mounted laser scanning system integrates sensors such as laser scanner (LiDAR, Light Detection AndRanging), satellite positioning system (GNSS, Global Navigation Satellite System) and inertial navigation system (INS, Inertial Navigation System) in the mobile platform. It collects the three-dimensional spatial information and attribute information of roads and surrounding features in a medium and efficient manner, and is widely used in road surveys, road facility surveys, and lane-level high-precision map production. [0003] The vehicle-mounted laser point cloud is the main data achievement of the vehicle-mounted laser scanning system. Since th...

AI Technical Summary

Problems solved by technology

[0006] 1) Due to the point density of the vehicle-mounted laser point cloud, the filtering efficiency of the existing vehicle-mounted laser point cloud filtering algorithm is low;

[0007] 2) Due to the uneven distribution of the vehicle-mounted laser point cloud, existing filtering algorithms have difficulties in setting the filtering window size or grid size;

[0008] 3) There is a large area of ​​data holes caused by occlusion in the vehicle-mounted laser point cloud. The existing filtering method processes the occlusion area by interpolation, which brings errors to the filtering results;

[0009] 4) There are a large number of large-scale ground objects and small-scale terrain details in the vehicle-mounted laser point cloud. The existing filtering algorithm cannot filter out large-scale ground objects while retaining small-scale terrain details.

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