RODec 22, 2021

Semantically enriched spatial modelling of industrial indoor environments enabling location-based services

arXiv:2112.11856v13 citations
Originality Synthesis-oriented
AI Analysis

This addresses the need for efficient location-based services in industrial settings like intralogistics and production, though it appears incremental as it builds on reviewed approaches from other domains.

The paper tackles the problem of developing location-based services for industrial indoor environments by proposing RAIL, a software system that creates dynamic spatial models integrating sensor data and contextual information through a unified interface. The result is a novel modelling approach and architecture specifically designed for intralogistics and production domains.

This paper presents a concept for a software system called RAIL representing industrial indoor environments in a dynamic spatial model, aimed at easing development and provision of location-based services. RAIL integrates data from different sensor modalities and additional contextual information through a unified interface. Approaches to environmental modelling from other domains are reviewed and analyzed for their suitability regarding the requirements for our target domains; intralogistics and production. Subsequently a novel way of modelling data representing indoor space, and an architecture for the software system are proposed.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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