A robot environment perception method and system, a robot, a medium, and a product

CN122384784BActive Publication Date: 2026-10-16SWANCOR ADVANCED MATERIALS CO LTD
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Patent Information

Application Number
CN202610845899.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-06-12
Publication Date
2026-10-16
Estimated Expiration
2046-06-12

AI Technical Summary

Technical Problem

此类方案在硬件资源充裕的条件下能够获得较为全面的环境感知结果，但其固有机制决定了在嵌入式平台上，两类计算任务会直接争夺有限的GPU/CPU与显存带宽资源

Benefits of technology

[0010]In real-time environmental perception of embedded mobile robots, geometric tracking and semantic reasoning are tightly coupled and run in parallel, competing for limited computing power, making it impossible to simultaneously guarantee positioning accuracy and high-quality semantic mapping. This application provides a robot environmental perception method, system, robot, computer-readable storage medium, and computer program product. By acquiring keyframe pose data, scene structure data, and semantic processing frames, and then performing semantic perception processing on the semantic processing frames and fusing them to generate semantic map data, geometric data acquisition and semantic reasoning are decoupled in time. Thus, the geometric tracking process is no longer delayed by semantic reasoning, ensuring the stability and accuracy of real-time positioning; while semantic reasoning can be performed independently using the acquired geometric prior data, without real-time constraints, enabling the construction of a high-quality semantic map. This effectively solves the problem of positioning frame loss and map drift caused by computing power contention on embedded platforms, achieving a good balance between real-time geometric positioning and high-precision semantic mapping.

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Abstract

The application discloses a robot environment perception method and system, a robot, a medium and a product. The method comprises the following steps: acquiring key frame pose data containing image data, scene structure data representing the association between an image frame and a target environment space, and semantic processing frames determined from the image frame; performing semantic perception processing on the semantic processing frames to obtain first semantic instance information, and / or matching and obtaining second semantic instance information from a semantic model; and performing fusion processing based on the obtained semantic instance information to generate or update semantic map data containing semantic road signs. The scheme decouples the geometric data acquisition and the semantic reasoning task in time sequence, so that the real-time positioning and tracking are not blocked by the semantic reasoning delay, the semantic reasoning can be triggered as needed, and the real-time positioning performance and the semantic map construction quality are considered under limited computing resources.
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