Real-time track obstacle detection method based on three-dimensional point cloud

Abstract

The invention discloses a real-time track obstacle detection method based on a three-dimensional point cloud, and the method comprises the steps: carrying out the processing of three-dimensional point cloud sequence data collected by a laser radar, firstly carrying out the coordinate transformation of the point cloud, converting the coordinates in a European coordinate system into the coordinates in a spherical coordinate system, putting each point in the point cloud into a certain voxel of a cone by using a cone voxelization down-sampling method so as to reduce the calculation amount of subsequent steps; inputting the downsampled points into a local feature coding module, searching local point clouds by using K-nearest neighbor (KNN), aggregating geometric features of the local point clouds, and connecting the centroid, neighbor point coordinates, relative coordinates and Gaussian density features of the local point clouds into a vector; connecting all local point cloud information into a matrix through traversal, and obtaining high-dimensional local feature information of each local point cloud through MLP and maximum pooling; and finally, utilizing multi-scale three-dimensional sparse convolution to realize track real-time identification of a single-frame image through a plurality of down-sampling and up-sampling modules.

Description

technical field [0001] The invention relates to a semantic segmentation technology based on three-dimensional point cloud vision, in particular to a method for judging whether an obstacle exists on a rail transit. Background technique [0002] Railway transportation is an important carrier of personnel flow, and is recognized as a comfortable, fast and safe transportation method. Safety monitoring has always been an important part of railway transportation automation. However, the complex environment of the railway laying area has brought great challenges to train safety. However, at present, most routes still rely on the driver's judgment to ensure driving safety, such as judging whether the front is safe by identifying signal lights. With the application of smart technology, vehicle assistance systems based on on-board sensors have been developed for many years. In this case, it is an effective way to develop a train assisted driving system based on on-board sensors. O...

AI Technical Summary

Problems solved by technology

[0005] The invention aims to overcome the shortcomings of the existing technology that the camera is easily affected by enviro

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