A point cloud semantic map construction method based on deep learning and lidar

Abstract

The present invention relates to the technical field of intelligent semantic maps, and more specifically, to a point cloud semantic map construction method based on deep learning and laser radar. The method includes: step 1: constructing a deep convolutional neural network; step 2: preprocessing the lidar point cloud; step 3: training the deep convolutional neural network model; step 4: inputting the lidar point cloud into the deep convolutional neural network model , to get the label of each point in the lidar point cloud, that is, the point cloud with semantic annotation; step 5: use the lidar point cloud with semantic annotation to construct a real-time point cloud map, and obtain the point cloud semantic map; step 6: After the point cloud semantic map is constructed, the semantic information in the point cloud map is globally optimized and corrected using voting based on sliding windows.

Description

technical field [0001] The present invention relates to the technical field of intelligent semantic maps, and more specifically, to a point cloud semantic map construction method based on deep learning and laser radar. Background technique [0002] Lidar point cloud maps play an important role in the perception and motion planning of autonomous robots (including but not limited to: autonomous vehicles, robots, unmanned aerial vehicles, etc.). There are many methods for generating laser point cloud maps based on lidar. LiDAR point cloud maps typically consist of large-scale, discrete points. Therefore, the lidar point cloud map lacking semantic information is more difficult for human eyes to identify, which is not conducive to manual post-processing. At the same time, due to the lack of semantic information in the map, it is difficult for engineers to formulate more complex motion planning for robots. At present, there have been researches on semantic segmentation of objec...

AI Technical Summary

Problems solved by technology

In the process of using multiple sensors to construct static road networks and dynamic obstacle maps, it is necessary to perform a series of sensor calibration operations to fuse each sensor in advance. This calibration process is relatively complicated and has errors.

At the same time, GPS sensors cannot be used indoors; high-definition satellite images are difficult to obtain, so the process of building static road networks and obstacle maps with multiple sensors at the same time is cumbersome and not suitable for indoor scenarios and promotion

In the prior art, the semantic markup of the map is related to people, and each map needs to be manually marked separately, which is a large workload for the construction of large-scale semantic maps

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