Intelligent lawnmower autonomous mapping method, device, intelligent lawnmower and storage medium

By combining a monocular camera and LiDAR for autonomous mapping, the intelligent lawnmower can automatically identify and build high-precision 3D maps, solving the problems of low efficiency and poor quality of traditional manual mapping. This achieves adaptability and reliability to complex environments and improves the user experience.

CN122131756APending Publication Date: 2026-06-02QINGTING INTELLIGENT TECHNOLOGY (SUZHOU) CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
QINGTING INTELLIGENT TECHNOLOGY (SUZHOU) CO LTD
Filing Date
2026-01-21
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing intelligent lawn mowers require manual operation by users during the mapping process, resulting in low efficiency, poor quality, and low reliability. They are also difficult to adapt to complex environments, affecting the effectiveness of automated lawn mowing.

Method used

By combining a monocular camera with LiDAR, autonomous mapping is achieved through visual recognition, coordinate dimensionality upscaling, and path fusion. The camera captures environmental images, while the LiDAR collects point cloud data. Boundary lines are identified and 3D coordinates are calculated. These are then fused to generate a 3D guidance path, which controls the lawnmower to move autonomously and construct the map.

Benefits of technology

It achieves fully automatic mapping without manual user operation, improves mapping accuracy and adaptability, ensures that the map matches the actual boundary, avoids duplicate coverage and omissions, and enhances the reliability of automated lawn mowing.

✦ Generated by Eureka AI based on patent content.

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Abstract

This application relates to the field of garden robot technology, and discloses an autonomous mapping method, device, intelligent lawnmower, and storage medium for an intelligent lawnmower. The method includes: simultaneously acquiring environmental images and point cloud data via a camera and LiDAR; identifying the boundary of the area to be mowed based on the images and obtaining its two-dimensional coordinates; calculating the three-dimensional spatial coordinates of the boundary using inverse perspective mapping, combined with the point cloud data; merging the current three-dimensional boundary with an existing path to update and generate a three-dimensional guide path; controlling the lawnmower to move along this path; and continuously acquiring sequential data during the movement to construct a complete three-dimensional environmental map of the area to be mowed. This application achieves fully automatic and high-precision mapping without manual intervention, significantly improving mapping efficiency and quality, and exhibiting good terrain adaptability.
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