Laser radar environment sensing method and system based on deep learning

Abstract

The invention discloses a laser radar environment sensing method and system based on deep learning. The method comprises the following steps: step 1, mapping laser radar point cloud into a depth map;converting each point of the laser radar from a spherical coordinate system to an image coordinate system; 2, executing a depth map semantic segmentation step; and step 3, mapping the semantic segmentation graph into a semantic segmentation point cloud graph. Laser radar spherical mapping is adopted, a stable mapping formula is used, the algorithm difficulty is low, and the development and maintenance cost is low; semantic segmentation is high in precision and multiple in segmentation types; and environment recognition data suitable for the automatic driving automobile is formed, so that the automatic driving automobile can identify surrounding objects conveniently.

Description

technical field [0001] The present invention relates to the field of deep learning, in particular to a deep learning-based lidar environment perception method and system. Background technique [0002] The United States is the country with the most in-depth research on autonomous vehicles. Its research on autonomous vehicles began in the 1970s and 1980s, and has been in a high-speed development stage since the 1980s. The DAPAR program of the US Department of Defense is extremely It has greatly promoted the development of self-driving cars and obtained a series of major research results. Domestic research on self-driving cars started in the 1980s, including research by National University of Defense Technology, Nanjing University of Science and Technology, and Tsinghua University. Until 2003, the National University of Defense Technology and FAW Group Corporation jointly developed the automatic driving system for Hongqi vehicles, marking that my country's automobile automatic...

AI Technical Summary

Problems solved by technology

However, the prior information is not static. If a new traffic light does not have prior information entered, it will cause trouble when the self-driving vehicle encounters it.

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