Laser Point Cloud Matching Method Based on Grouped Stepped Threshold Judgment

Abstract

The invention provides a laser point cloud matching method based on grouping stepped threshold judgment. The laser point cloud matching method comprises the following steps: S1, acquiring point clouddata M and N of two frames before and after a laser radar at the current moment; s2, dividing M and N into a first cloud data sub-group and a second cloud data sub-group of a fixed array; s3, performing iteration nearest point matching on each point cloud; s4, judging whether the matching rate of the first cloud data subgroup and the second cloud data subgroup is greater than a first preset threshold or not; if yes, indicating that matching succeeds, carrying out subsequent steps continuously, and otherwise, indicating that matching fails; s5, judging whether the matching group rate of successful matching of M and N is greater than a second preset threshold or not; if yes, indicating that matching succeeds, and otherwise, carrying out subsequent steps continuously; and S6, obtaining two groups of point cloud data of the front frame and the rear frame of the laser radar at the next moment as new M and N, and returning to the step S2. According to the laser point cloud matching method based on grouping stepped threshold judgment, the algorithm calculation amount can be reduced under the condition that the positioning precision is not reduced.

Description

technical field [0001] The invention relates to the field of robot navigation, in particular to a laser point cloud matching method based on grouping step threshold judgment. Background technique [0002] At present, robot positioning technology is widely used in park inspection, storage and transportation and other fields. The application of robot autonomous positioning and navigation technology can effectively replace humans to complete part of the work. Therefore, robot positioning and navigation technology is a current research hotspot. [0003] In the process of robot navigation, the surrounding environment is scanned by laser radar to realize the positioning of the robot itself. Among them, the difficulty of robot positioning technology lies in the identification and successful matching of surrounding obstacles. For example, during the lidar scanning process, the lidar will scan the same obstacle at different times and different positions, and it is necessary to match...

AI Technical Summary

Problems solved by technology

Due to the existence of problems such as the large amount of calculation at the closest point of iteration, the real-time performance of robot obstacle matching is low, the positioning effect is poor, and the obstacle avoidance function cannot be completed well.

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