Place Recognition via Knowledge Graph Inference
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Solution Overview
Problem
Current place recognition technologies face challenges in integrating heterogeneous information sources, leading to low recognition accuracy and a lack of unified inference, as well as poor semantic interpretability and visualization of the inference process.
Innovation Solution
A place recognition method based on knowledge graph inference is introduced, which involves acquiring basic semantic data, generating place description entities, constructing a place knowledge graph, and performing inference using a Deep Neural Network (DNN) to integrate and infer environmental information from various sources, enhancing recognition accuracy and semantic understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Deep Neural Network models are used for place recognition with heterogeneous information sources, then recognition coverage is improved, but recognition accuracy deteriorates due to lack of unified inference
Solution Approach 1:
The patent merges multiple independent DNN models that process different information sources (images, distances, sounds) into a single unified knowledge graph inference system. This integration allows heterogeneous information to be processed together through shared semantic concepts, achieving both comprehensive recognition coverage and high accuracy through unified logical inference.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary layer between raw sensor inputs and place recognition outcomes. The knowledge graph provides a standardized semantic framework that mediates the integration of heterogeneous information sources, enabling accurate unified inference while maintaining broad recognition coverage through its structured ontology.
2Productivity
If end-to-end DNN models are used for place recognition, then processing speed is improved, but semantic interpretability deteriorates due to loss of intermediate inference results
Solution Approach 1:
The patent segments the place recognition process into distinct inference stages represented as triples in the knowledge graph (subject-predicate-object). Each triple represents an intermediate inference result that maintains semantic meaning, allowing the system to preserve interpretability while achieving efficient processing through the structured, modular nature of knowledge graph reasoning.
3Adaptability or versatility
If independent knowledge graphs are constructed by different users for their application fields, then domain specificity is improved, but integration capability deteriorates due to absence of unified place knowledge graph
Solution Approach 1:
The patent creates a universal place knowledge graph that serves multiple domains and applications simultaneously. The graph's ontology is designed to be domain-agnostic yet adaptable, allowing it to function across different application fields (robotics, navigation, human-computer interaction) while maintaining the ability to integrate heterogeneous information sources through its standardized semantic framework.
Data Source
AI summary
The present disclosure discloses a place recognition method based on knowledge graph inference; and provides, on the basis of giving a knowledge graph construction method in the place field, a recognition method of general places that is based on knowledge graph inference and can integrate various heterogeneous environmental information, including the following steps: (1) extracting main clues such as the main items that make up the place, the produced events, and the spatial structure from various heterogeneous information, and describing these clues in natural language text; (2) screening the foregoing descriptions by using natural language processing methods, to form place description entities; (3) constructing a knowledge graph in the place field according to the occurrence frequencies of the description entities in an actual environment; and (4) implementing inference and classification based on the knowledge graph by using a Deep Neural Network (DNN), to give a final recognition result. The present disclosure improves the place recognition accuracy by means of knowledge graph inference, and greatly improves semantic interpretability in the place recognition process.