Ground point cloud segmentation method based on three-dimensional laser radar

Abstract

The invention discloses a ground point cloud segmentation method based on a three-dimensional laser radar. The ground point cloud segmentation method comprises the steps of: 1) acquiring polar coordinate data of three-dimensional laser radar scanning point cloud in the surrounding environment of a vehicle and converting the polar coordinate data to a local rectangular coordinate system; 2) correcting radar data by utilizing a vehicle-mounted IMU and an odometer; 3) constructing a polar coordinate grid map, and extracting an extension vertex in each grid according to vertical continuity of point cloud distribution in the grids; 4) extracting ground points in non-marginal grids according to height attributes of the extension vertexes and a ground smooth consistency criterion, and adopting a 3sigma criterion for further extraction of ground points in marginal grids. Compared with the prior art, the ground point cloud segmentation method can reduce ground extraction errors caused by motion of the vehicle, avoids the occurrence of over-segmentation and under-segmentation, is high in precision and high in reliability, has high real-time performance and can be widely used in the field of radar-based environment sensing technologies.

Description

technical field [0001] The invention relates to the field of unmanned driving technology, in particular to a ground point cloud segmentation method based on a three-dimensional laser radar. Background technique [0002] Three-dimensional lidar can obtain real-time and accurate environmental information around the vehicle and is rarely affected by illumination and weather changes, so it is widely used in the environmental perception system of unmanned vehicles. In the urban traffic environment, the most common obstacles are vehicles, pedestrians, trees, buildings, etc. To achieve accurate perception of these obstacles, these obstacles must first be accurately segmented from the radar data, and these obstacles They are all distributed on the ground and connected together by ground points, so the ground must be extracted first, otherwise the existence of ground points will make all the objects on the ground connected to each other, and the segmentation cannot be completed. The...

AI Technical Summary

Problems solved by technology

[0007] In order to solve the problems of missed detection of some point cloud data in the point cloud segmentation method of the prior art, inability to process a large amount of disordered point cloud data and poor real-time performance when processing a large amount of point cloud data, the present invention provides the following technical solutions to solve the problem:

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