Ring main unit inspection robot autonomous navigation method and system based on SLAM

By introducing semantic feature parsing and topological constraints into the SLAM system, and combining multi-source information optimization, the problems of unstable positioning and path deviation in ring network cabinet inspection were solved, and more stable autonomous navigation and relocation capabilities were achieved.

CN121541635APending Publication Date: 2026-02-17STATE GRID HUBEI ELECTRIC POWER CO XIAOGAN POWER SUPPLY CO
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Patent Information

Application Number
CN202511562985.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-29
Publication Date
2026-02-17

AI Technical Summary

Technical Problem

Existing SLAM technology suffers from unstable positioning accuracy, path tracking deviation, and low repositioning efficiency in ring main unit inspection scenarios, making it difficult to meet the requirements of power operation and maintenance for autonomy, real-time performance, and continuity. This is mainly due to positioning drift and error accumulation caused by sensor feature confusion and the influence of lighting conditions.

Method used

Semantic feature parsing and topological constraints are introduced. Data is collected through LiDAR, vision sensors and inertial measurement units, and deep learning is used to identify objects such as electrical cabinets. A set of semantic features is generated and a topological graph is constructed. The pose calculation is constrained and optimized by combining the inertial measurement results. The positioning health is calculated in real time and repositioning is triggered when there is a risk of deviation.

Benefits of technology

It improves the stability of pose calculation and navigation continuity in the ring network cabinet environment, reduces the impact of sensor interference on positioning accuracy, and ensures the robot's autonomous navigation capability in complex environments.

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Abstract

The invention discloses a ring main unit inspection robot autonomous navigation method and system based on SLAM, particularly relates to the technical field of robot autonomous navigation and intelligent inspection, and is used for solving the problem of positioning drift caused by repeated features of an existing ring main unit scene. Semantic feature analysis and topological constraints are introduced into an SLAM processing flow, acquired image data and point cloud data are processed through a deep learning model, objects such as an electrical cabinet, a corridor channel and a cable trench are identified, and a semantic feature set with category labels and spatial position information is generated; and constructing a topological graph containing node spacing, connectivity and directivity constraints based on the semantic features, adding the topological graph as a constraint factor into SLAM back-end optimization, and performing joint optimization in combination with vision, a laser odometer and inertial prior information, thereby avoiding only depending on repeated geometric feature positioning, and improving the positioning accuracy. The problems of loopback misjudgment and drifting caused by feature confusion are reduced, and the pose resolving stability in the ring main unit environment is improved.
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