A method and system for matching laser radar SLAM and a vegetation feature library of a mowing robot

By using a LiDAR SLAM matching method with a vegetation feature database, the problems of inaccurate positioning and difficulty in vegetation identification of lawnmower robots in complex environments were solved. This enabled high-precision environmental map construction and intelligent path planning, improving lawnmower efficiency and robot adaptability.

CN120778126BActive Publication Date: 2026-04-14YITUO OUTDOOR TECH LTD
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Patent Information

Application Number
CN202510699976.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-05-27
Publication Date
2026-04-14
Estimated Expiration
2045-05-27

AI Technical Summary

Technical Problem

Existing lawn mowing robots cannot effectively handle lawn mowing tasks in complex environments, especially due to complex terrain and unknown cutting targets, resulting in inaccurate positioning and inability to effectively identify vegetation types.

Method used

By matching LiDAR SLAM with a vegetation feature database, LiDAR test data of various vegetation samples are obtained, initial LiDAR parameters are determined, a vegetation feature database is established, and LiDAR parameters are adjusted based on environmental influencing factors to generate current LiDAR parameters, identify vegetation types, and update 2D raster or 3D point cloud maps.

Benefits of technology

It improves the positioning accuracy and vegetation recognition capabilities of lawnmower robots, reduces positioning errors caused by environmental factors, enables more intelligent path planning, improves mowing efficiency, and reduces energy consumption.

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Abstract

The application relates to a laser radar SLAM and vegetation feature library matching method and system of a mowing robot, and relates to the technical field of data processing. The method comprises the following steps: determining initial laser radar parameters based on laser radar test data of a plurality of sample vegetation; establishing a vegetation feature library based on the initial laser radar parameters and the laser radar test data of the plurality of sample vegetation; determining environmental influence factors based on the initial laser radar parameters; acquiring real-time environmental information of the mowing robot; adjusting the initial laser radar parameters based on the environmental influence factors and the real-time environmental information to generate current laser radar parameters; acquiring point clouds of vegetation to be matched based on the current laser radar parameters; determining a vegetation type based on the point clouds of the vegetation to be matched and the vegetation feature library; and updating a two-dimensional grid map or a three-dimensional point cloud map of an environment where the mowing robot is located based on the point clouds of the vegetation to be matched and the vegetation type, so that the intelligent level of the mowing robot is improved.
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