Unmanned vehicle self-positioning method based on three-dimensional laser radar

Abstract

The invention relates to an unmanned vehicle positioning algorithm based on a three-dimensional laser radar. The unmanned vehicle positioning algorithm includes the following steps: 1. extracting road edge feature points from a single frame of laser point cloud data, and according to a vehicle kinematic model, converting multiple frames of detected road edge feature point coordinates into a current vehicle coordinate system; 2. utilizing a Beam model method to extract the outlines of the multiple frames of road edge feature points; and 3. matching the road edge outline extracted in the Step 2 with high-precision map data, calculating lateral, longitudinal and course angle deviations of a current vehicle as observation values, and inputting the observation values into Kalman filters to perform vehicle pose estimation. Long-time long-distance stable positioning of the vehicle can be guaranteed. The method is applicable to various laser sensors, and can also obtain relatively high positioning accuracy. Abnormal points can be effectively removed, so that map matching is more accurate, and positioning accuracy is higher.

Description

technical field [0001] The invention relates to an autonomous positioning method for an unmanned vehicle. Background technique [0002] State of the Art and Its Defects [0003] With the development of computer technology and artificial intelligence, unmanned vehicles (hereinafter referred to as unmanned vehicles) have shown great application prospects in military, transportation, industrial production, logistics and warehousing, and daily life. In terms of national defense and military affairs, unmanned vehicles are mostly used to perform military tasks in dangerous scenarios, such as military rescue and material delivery. In terms of traffic safety, unmanned driving technology is an effective means to promote the development of intelligent transportation systems. Unmanned driving technology based on artificial intelligence can improve the active safety of vehicles, and can effectively reduce traffic accidents caused by driver misuse, thereby Improve traffic efficiency an...

AI Technical Summary

Problems solved by technology

[0009] Although the positioning algorithm based on the feature level has the advantages of simple structure and low computational complexity, there are some limitations when it is applied to the positioning of unmanned vehicles and the estimation of the state of nonlinear systems in complex environments.

When an unmanned vehicle is driving in a complex environment, it is difficult to associate the feature points detected by the on-board sensors with the map data. The reason is that there will be false detections and obstacles in the detection of feature points. match, which will cause the positioning algorithm to fail

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