Plane feature fusion method in the process of slam mapping

Abstract

The invention relates to a plane feature fusion method in the process of SLAM mapping, belonging to the field of image processing. The steps are: 1) According to the point cloud data of the vehicle lidar, segment the plane features in the lidar coordinate system at different times; 2) Obtain the vehicle GPS position and IMU heading angle information at different continuous times, so as to obtain the translation matrix relative to the initial position and rotation matrix; 3) Transform all plane feature information in the lidar coordinate system to the world coordinate system; 4) Angle size for the intersection line between any plane feature and the x‑o‑y plane at any two different times The calculation of the calculation of the coincidence degree of the feature circle and the calculation of the relative distance; 5) Delete the class with a small number of features in the class, and find the corresponding linear regression equation for the features of the remaining classes, and then construct the world coordinate system. global map. The invention reduces redundant features, improves the simplicity of map expression and the precision of map construction.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a plane feature fusion method in a SLAM mapping process. Background technique [0002] Vehicle-mounted lidar can accurately perceive the surrounding environment of the vehicle and has the advantages of fast response and long ranging. Structured buildings are common obstacles in urban traffic environments. How to construct a map corresponding to a building from a 3D point cloud is one of the important issues for vehicles to perceive the environment. In general, planar features can represent the surface of a structured building in the real environment, but the same building surface is likely to be repeated in multiple consecutive frames, eventually resulting in redundant features in the planar feature map. These redundant features should be properly fused to avoid ambiguity in map representation and reduce computation. [0003] The existing plane feature fusion methods mainly inclu...

AI Technical Summary

Problems solved by technology

[0005] (1) The fused features still contain more redundant features, so the constructed map is not concise enough

[0006] (2) The existence of accumulated errors in the fusion process leads to low accuracy of the constructed map

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