A Conceptual Framework for Indoor Mapping by Using Grammars

Hu, Xuke; Fan, Hongchao; Zipf, Alexander; Shang, Jing

doi:10.5194/isprs-annals-iv-2-w4-335-2017

Cited by 5 publications

(4 citation statements)

References 11 publications

(14 reference statements)

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“…The problem of understanding the semantics of indoor scenes at room level has been studied as a room classification or room categorization task in social robotics research [16,22] and as a room semantic labeling task in indoor location-based research [18,31,32]. Social robots are widely used in smart home services such as assisting seniors and doing housekeeping, and it is an essential skill for a robot to recognize the types of rooms it is navigating in a house [33].…”

Section: Related Workmentioning

confidence: 99%

Automated Generation of Room Usage Semantics from Point Cloud Data

Cai,

Pan

2023

IJGI

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Room usage semantics in models of large indoor environments such as public buildings and business complex are critical in many practical applications, such as health and safety regulations, compliance, and emergency response. Existing models such as IndoorGML have very limited semantic information at room level, and it remains difficult to capture semantic knowledge of rooms in an efficient way. In this paper, we formulate the task of generating rooms usage semantics as a special case of room classification problems. Although methods for room classification tasks have been developed in the field of social robotics studies and indoor maps, they do not deal with room usage and occupancy aspects of semantics, and they ignore the value of furniture objects in understanding room usage. We propose a method for generating room usage semantics based on the spatial configuration of room objects (e.g., furniture, walls, windows, doors). This method uses deep learning architecture to support a room usage classifier that can learn spatial configuration features directly from semantically labelled point cloud (SLPC) data that represent room scenes with furniture objects in place. We experimentally assessed the capacity of our method in classifying rooms in office buildings using the Stanford 3D (S3DIS) dataset. The results showed that our method was able to achieve an overall accuracy of 91% on top-level room categories (e.g., offices, conference rooms, lounges, storage) and above 97% accuracy in recognizing offices and conference rooms. We further show that our classifier can distinguish fine-grained categories of of offices and conference rooms such as shared offices, single-occupancy offices, large conference rooms, and small conference rooms, with comparable intelligence to human coders. In general, our method performs better on rooms with a richer variety of objects than on rooms with few or no furniture objects.

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Section: Related Workmentioning

confidence: 99%

Automated Generation of Room Usage Semantics from Point Cloud Data

Cai,

Pan

2023

IJGI

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“…This group of approaches uses the structural rules or features in a certain building type to assist the reconstruction of maps. Such rules or features can be gained through manual definitions (Becker, Peter, & Fritsch, 2015; Hu, Fan, Zipf, Shang, & Gu, 2017; Yue, Krishnamurti, & Grobler, 2011) or machine learning techniques (Rosser, Smith, & Morley, 2017). Yue et al.…”

Section: Related Workmentioning

confidence: 99%

Room semantics inference using random forest and relational graph convolutional networks: A case study of research building

Fan²,

Noskov

et al. 2020

Transactions in GIS

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Semantically rich maps are the foundation of indoor location‐based services. Many map providers such as OpenStreetMap and automatic mapping solutions focus on the representation and detection of geometric information (e.g., shape of room) and a few semantics (e.g., stairs and furniture) but neglect room usage. To mitigate the issue, this work proposes a general room tagging method for public buildings, which can benefit both existing map providers and automatic mapping solutions by inferring the missing room usage based on indoor geometric maps. Two kinds of statistical learning‐based room tagging methods are adopted: traditional machine learning (e.g., random forests) and deep learning, specifically relational graph convolutional networks (R‐GCNs), based on the geometric properties (e.g., area), topological relationships (e.g., adjacency and inclusion), and spatial distribution characteristics of rooms. In the machine learning‐based approach, a bidirectional beam search strategy is proposed to deal with the issue that the tag of a room depends on the tag of its neighbors in an undirected room sequence. In the R‐GCN‐based approach, useful properties of neighboring nodes (rooms) in the graph are automatically gathered to classify the nodes. Research buildings are taken as examples to evaluate the proposed approaches based on 130 floor plans with 3,330 rooms by using fivefold cross‐validation. The experiments conducted show that the random forest‐based approach achieves a higher tagging accuracy (0.85) than R‐GCN (0.79).

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“…This group of approaches uses the structural rules or knowledge of a certain building type to assist the reconstruction of maps. The rules can be gained through manual definitions [27,[47][48][49] or machine learning techniques [19,[50][51][52]. Yue et al [48] proposed using a shape grammar that represents the style of Queen Anne House to reason the interior layout of residential houses with the help of a few observations, such as footprints and the location of windows.…”

Section: Related Workmentioning

confidence: 99%

“…The procedure of the proposed approach is as follows: We first obtain the frequency and the parameters of the multivariate Gaussian distribution of each room type from training rooms. Then, we improve the rules defined in the previous work [27] by removing a couple of useless rules and adding a couple of useful rules in semantic inference and changing the format of rules for the purpose of generating models to the format of rules for sematic inference. The next step is to partition these rules into multiple layers based on their dependency relationship.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Using Grammars to Infer Room Semantics

Fan

Noskov

et al. 2019

Remote Sensing

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Current indoor mapping approaches can detect accurate geometric information but are incapable of detecting the room type or dismiss this issue. This work investigates the feasibility of inferring the room type by using grammars based on geometric maps. Specifically, we take the research buildings at universities as examples and create a constrained attribute grammar to represent the spatial distribution characteristics of different room types as well as the topological relations among them. Based on the grammar, we propose a bottom-up approach to construct a parse forest and to infer the room type. During this process, Bayesian inference method is used to calculate the initial probability of belonging an enclosed room to a certain type given its geometric properties (e.g., area, length, and width) that are extracted from the geometric map. The approach was tested on 15 maps with 408 rooms. In 84% of cases, room types were defined correctly. It, to a certain degree, proves that grammars can benefit semantic enrichment (in particular, room type tagging).

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A Conceptual Framework for Indoor Mapping by Using Grammars

Cited by 5 publications

References 11 publications

Automated Generation of Room Usage Semantics from Point Cloud Data

Automated Generation of Room Usage Semantics from Point Cloud Data

Room semantics inference using random forest and relational graph convolutional networks: A case study of research building

Feasibility of Using Grammars to Infer Room Semantics

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