Multi-sensor fusion road extraction and indexing method based on global and local grid maps

A technology of multi-sensor fusion and road extraction, applied in still image data indexing, still image data retrieval, instruments, etc., can solve the problems of single semantic information, low efficiency, and high degree of specialization, and achieve efficient data processing, compressed data, The effect of efficient storage and recall

Active Publication Date: 2020-06-12
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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Problems solved by technology

[0003] In the current high-precision map drawing scheme, a large number of steps such as laser point cloud conversion, splicing, labeling and indexing are required, and the process is very complicated and requires professional technicians
This makes high-precision maps expensive, highly specialized, and inefficient in production
In addition, in the usual high-precision map production process, there is n

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  • Multi-sensor fusion road extraction and indexing method based on global and local grid maps
  • Multi-sensor fusion road extraction and indexing method based on global and local grid maps
  • Multi-sensor fusion road extraction and indexing method based on global and local grid maps

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[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0059] See figure 1 The present invention proposes a method for road extraction and indexing based on multi-sensor fusion of global and local grid graphs, which includes the following steps:

[0060] Step 1. Establish global and local grid maps, and establish the association between the local grid maps and the global grid maps according to vehicle positioning information;

[0061] Step 2. Extract the laser point cloud raster with the aver...

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Abstract

The invention discloses a multi-sensor fusion road extraction and indexing method based on global and local grid maps. Data storage and preprocessing are carried out by adopting global and local gridmaps; the real-time position and posture of an unmanned vehicle are obtained through a differential positioning system. High-precision road boundaries lane lines and road sign positions are obtainedthrough laser radar point cloud related data stored in the grid map and projected into the monocular vision image, the lane line type is judged based on a deep learning method, road sign semantic information is recognized, and therefore a high-precision road map with semantic information is obtained. Efficient storage and indexing are realized in a mode of establishing multiple global sections androad sets.

Description

technical field [0001] The invention belongs to the technical field of automatic driving, and relates to an efficient and high-precision semantic road map construction method and a visualization method thereof. Background technique [0002] High-precision maps are of great significance in the field of autonomous driving. A high-precision map, also called a high-resolution map, is a map specially designed for driverless driving. Compared with traditional maps, high-precision maps can not only provide road-level navigation information, but also lane-level positioning and navigation information. [0003] In the current high-precision map drawing scheme, a large number of steps such as laser point cloud conversion, splicing, labeling and indexing are required, and the process is very complicated and requires professional technicians. This makes high-precision maps expensive, highly specialized, and inefficient in production. In addition, in the usual high-precision map produc...

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Application Information

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IPC IPC(8): G01S17/06G01S17/87G01S17/931G06F16/51G06F16/587G06F16/583G06Q50/30
CPCG01S17/06G01S17/87G06F16/51G06F16/587G06F16/583G06Q50/30
Inventor 祝辉余泽海梁华为王智灵林玲龙余结
Owner HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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