Method and apparatus for identifying laser point cloud data of unmanned vehicles

Abstract

The present application discloses a method and apparatus for identifying laser point cloud data of an unmanned vehicle. The unmanned vehicle is provided with a lidar, and a specific embodiment of the method includes: obtaining the current pose information of the unmanned vehicle in the world coordinate system in response to receiving the latest frame of laser point cloud data collected by the lidar; According to the current pose information, N×N map blocks that are preloaded into the cache and centered on the map block corresponding to the current pose information in the preset three-dimensional grid map are obtained from the cache; for the received For each laser point data in the laser point cloud data, the laser point data recognition operation is performed. This embodiment realizes the identification of whether each laser point data is a static laser point, provides a basis for the subsequent analysis and processing of the laser point data by unmanned vehicles, and can improve the accuracy of laser point data obstacle recognition.

Description

technical field [0001] The present application relates to the field of unmanned vehicles, in particular to the field of obstacle identification, and in particular to a method and device for identifying laser point cloud data of unmanned vehicles. Background technique [0002] Currently, lidar is installed in most driverless vehicles. The unmanned vehicle analyzes and processes the laser point cloud data collected by the lidar, and finally performs route planning and driving control. When analyzing and processing the laser point cloud, the most important thing is to identify obstacles for each laser point data in the laser point cloud data, that is, given that each laser point data in the laser point cloud data represents the physical world what kind of obstacles. Existing obstacle recognition methods for laser point data are mainly rule-based methods or machine learning-based methods. [0003] However, the rule-based method is difficult to find and use appropriate rules, ...

AI Technical Summary

Problems solved by technology

[0003] However, the rule-based method is difficult to find and use appropriate rules, and it is too difficult to exhaust the types of obstacles in the real world

In addition, the method based on machine learning has the problem that it may not be able to recognize the types of obstacles that have not been trained

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