AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The results demonstrate the capability of the proposed framework to support scene understanding, generate scene graphs, and enable LLM-based task planning for robot-based maintenance and inspection of civil infrastructure.
The study argues that scene perception and task semantic understanding constitute a continuous intelligent chain from "environment recognition" to "task execution" so as to enhance their practical performance and operational reliability.
NIKG occupies the space between scene-oriented traffic KGs and intention-prediction models: a small, explicit semantic layer intended to support downstream work such as knowledge graph embedding, SPARQL-based feature extraction, or hybrid neuro-symbolic reasoning.
A hybrid pipeline for semantic mapping is presented that incorporates an external calibrated camera using homography projection for geometric mapping and localization, combined with object detection, persistent object tracking and ontology driven semantic updates to build a dynamic semantic world model.
This framework combines simultaneous localization and mapping (SLAM), visual-language feature extraction, incremental semantic and instance label fusion, and spatial graph construction to enable a construction robot navigation framework that supports open-vocabulary language queries.
BIM-GRASP is the first framework of its kind to enable the parsing of IFC data as a knowledge graph through natural language without requiring specialized technical expertise or manual schema navigation, and significantly improves precision and relevance in information extraction.
The Hypermap framework is presented, which can manage multiple maps of different types and the capabilities of the framework to handle occupancy grid layers and semantic polygonal layers are explored, but the framework can be extended with new layer types in the future.
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