Automatic Room Detection and Room Labeling from Architectural Floor Plans

Modern automation demands mobile robots to be robustly localized in complex scenarios. Current localization systems typically use maps that require to be built and interpreted by experienced operators, increasing deployment costs as well as reducing the adaptability of robots to rearrangements in the environment. In contrast, architectural floor plans can be easily understood by non-expert users and typically represent only the non-rearrangeable parts of buildings. In this paper we propose a system for robot localization in architectural CAD drawings. Our method employs a simultaneous localization and mapping approach to online augment the floor plan with a map represented as a pose-graph with LiDAR measurements. Whenever the environment is accurately mapped in the vicinity of the robot, we use the graph to perform relative localization. We thoroughly evaluate our system in challenging real-world scenarios. Experiments demonstrate that our method is able to robustly track the robot pose even when the floor plan shows major discrepancies from the real-world. We show that our system achieves sub-centimeter accuracy and is suitable for real-time application.

Recently, indoor space construction information has been actively carried out primarily in large buildings and in underground facilities. However, the building of this data was done by only a handful of people, and it was a time- and money-intensive task. Therefore, the technology of automatically extracting a wall and constructing a 3D model from architectural floor plans was developed. Complete automation is still limited by accuracy issues, and only a few sets of floor plan data to which the technology can be applied exist. In addition, it is difficult to extract complicated walls and their thickness to build the wall-junction structure of indoor spatial information, which requires significant topological information in the automation process. In this paper, we propose an automatic method of extracting the wall from an architectural floor plan suitable for the restoration of the indoor spatial information according to the indoor spatial information standard.

Mobile robots, in modern technology, demand a more robust localization in a complex environment. Currently, the most commonly used 2D LiDAR localization system for mobile robots requires maps that are constructed by 2D SLAM. Such systems do not cope well with dynamic environments and also have high deployment costs when moving robots to a new environment setting as they require the reconstruction of a map for each new place. In modern days, a floor plan is indispensable for an indoor environment. It typically represents essential structures such as walls, corners, pillars, etc. for humans to navigate in the environment. This information turns out to be crucial for robot localization. In this paper, we propose an approach for 2D LiDAR localization in an architectural floor plan. We use partial simultaneous localization and mapping (PSLAM) algorithm to generate a map while we concurrently aligned it to the floor plan using Monte Carlo Localization (MCL) method. Real-world experiments have been conducted with our proposed method which results in robust robot localization, the algorithm is even evaluated on a large discrepancies floor plan (discrepancies between the floor plan and real-world). Our algorithm demonstrates that its capabilities of localizing in real-time applications.

Most studies on map segmentation and recognition are focused on architectural floor plans, while there are very few analyses of shopping mall plans. The objective of the work is to accurately segment and recognize the shopping mall plan, obtaining location and semantic information for each room via segmentation and recognition. This work can be used in other applications such as indoor robot navigation, building area and location analysis, and three-dimensional reconstruction. First, we identify and match the catalog of a mall floor plan to obtain matching text, and then we use the two-stage region growth method we proposed to segment the preprocessed floor plan. The room number is then obtained by sending each segmented room section to an OCR (optical character recognition) system for identification. Finally, the system retrieves the matching text to match the room number in order to obtain the room name, and outputs the needed room location and semantic information. It is considered a successful detection when a room region can be successfully segmented and identified. The proposed method is evaluated on a dataset including 1340 rooms. Experimental results show that the accuracy of room segmentation is 92.54%, and the accuracy of room recognition is 90.56%. The total detection accuracy is 83.81%.

The paper describes an intelligent computer aided architectural design system (ICAAD) called ICADS. ICADS encapsulates different types of design knowledge into independent "critic" modules. Each "critic" module possesses expertise in evaluating an architect's work in different areas of architectural design and can offer expert advice when needed. This research focuses on the representation of spatial information encoded in architectural floor plans and the representation of expert design knowledge. Described in the paper is our research in designing and developing two particular "critic" modules. The first module, FPDX, checks a residential apartment floor plan, verifies that the plan meets a set of government regulations, and offers suggestions for floor plan changes if regulations are not met. The second module, IDX, analyzes room and furniture layout according to a set of interior design guidelines and offers ideas on how furniture should be moved if the placement does not follow good design principles.

The recent years have seen the increasing importance of cognitive models for improved robot navigation. In this paper, a novel cognitive navigation package, which consists of topometric map representation and a three-level path planner, is proposed. The topometric maps are built from architectural floor plans with additional features within a limited number of selected regions. The inherent discrepancies between floor plans and the robot’s actual sensory readings are handled by the three-level path planner. The unique feature of this approach is that accurate localization is only required at the selected regions. At the other regions the robot will rely on the guiding directions towards the goal rather than on its accurate position on the map for navigation. Experiments show that our approach can endow a robot with capability for semantic interpretation and localization in unseen and dynamic environments.

We propose a spatial plan generator based on our spatial planning algorithm and spatial growth rules, combine it with evolutionary multi-objective optimization, apply the system to architectural room floor planning, and evaluate its potential as a support tool for planning. We also introduce a framework for combining the architectural room floor planning support system with an interactive evolutionary computation component that constitute the next step of our research. The system executes two test runs, evolving the generated room floor plans with four and six objectives, respectively, while we monitor the convergence of each of these objectives. The results show that all objectives converged and a great variety of architectural room floor plans was produced. Obtained plans include a variety of unexpected plans for an architect, which implies that this system is useful for not only beginners who need design support but also for architectural professionals.

The paper attempts a notational system which can sufficiently describe any given architectural floor plan with the capacity to do spatial reasoning. By its descriptive power the system certainly is a good representational tool for constructing spatial configuration. By its reasoning power the system can have some semantical understanding of the spatial configurations under construction. That is, the system knows some spatial properties such as: whether this space can have sufficient daylight? Can that space be seen from this space?

Nowadays, in contrast to the increasing needs for 3D building models reconstructed with both indoor and outdoor structure and semantic information, the current state-of-the-art methods are not able to reconstruct the indoor and outdoor of buildings as a whole in one workflow. This paper proposes an integrated framework for the reconstruction of full 3D building models, in which both 3D indoor and outdoor model are reconstructed in a collaborative manner by fusing airborne laser scanning (ALS) data, terrestrial laser scanning (TLS) data and architectural plans. As a proof of concept, a preliminary test to reconstruct integrated 3D facade and indoor model is presented. First, based on plane sweep technique, a semantic and geometric information integrated point matching based method is developed to register 2D floor plans with TLS points. Then based on the registration, 3D façade model and indoor model are reconstructed and integrated simultaneously. The test results demonstrate the feasibility of the proposed framework.

Automatic analysis and sketch-based retrieval of architectural floor plans

This paper introduces a novel AI-based framework for automated 2D architectural floor plan generation from natural language descriptions, employing graph-conditioned denoising diffusion probabilistic models (DDPM) integrated with a U-Net architecture. The proposed system transforms user-provided textual specifications of room types, counts, and spatial relationships into structured room-adjacency graphs. These semantic graphs are encoded into compact embeddings and used to condition the diffusion model during the generative process. The framework produces segmented floor plans with clearly labelled architectural components, which are further refined through post-processing into editable 2D layouts. We evaluate our method on the RPLAN dataset and incorporate qualitative assessments from domain experts. Results demonstrate superior performance in generating diverse, semantically valid, and spatially coherent floor plans compared to existing GAN- and transformer-based approaches. This work highlights the effectiveness of combining semantic graph representations with diffusion-based generative models in advancing AI-driven architectural design.

Traditionally, building floor plans are designed by architects with their usability, functionality and architectural aesthetics in mind; however, the structural properties of the distribution of load‐bearing walls and columns are usually not taken into account at this stage. In this paper, we propose a novel approach for the design of architectural floor plans by integrating structural layout analysis directly into the planning process. In order to achieve this, we introduce a planning tool which interactively enforces checks for structural stability of the current design, and which on demand proposes how to stabilize it if necessary. Technically, our solution contains an interactive architectural modelling framework as well as a constrained optimization module where both are based on respective architectural rules. Using our tool, an architect can predict already in a very early planning stage whose designs are structurally sound such that later changes due to stability reasons can be prevented. We compare manually computed solutions with optimal results of our proposed automated design process in order to show how much our proposed system can help architects to improve the process of laying out structural models optimally.

“Space allocation” was a central pursuit in postwar research on computing and architecture. Researchers sought an algorithm that could automatically design the most efficient floor plan for a set of activities. In this article, we connect an early algorithm for computing architectural floor plans to the postwar British hospital. We examine how researchers adapted algorithmic methods for floor layout design developed in industrial capitalist settings to the promotion of the British welfare state. Finally, we pay special attention to the agency that certain graphic inscriptions borrowed from mathematics had in validating these particular algorithmic methods as promising avenues for the algorithmic automation of all architectural work. This article situates the automation of hospital design in postwar U.K. at the intersection of building science and healthcare management, with the aim to contribute critical perspectives on algorithmic reifications of work in early computer-aided architectural design systems.

With the rapid increase of users, online meeting platforms have accumulated massive meeting transcripts. However, it is still a challenge for users to quickly master the chief information and manage the meetings, despite there are already some useful text summarization models. In this paper, a Knowledge Graph-based Meeting Summarization Framework is proposed to tackle this challenge. First, a two-layers meeting domain Knowledge Graph is developed to integrate more information of meetings. Based on which, an encoder-decoder architecture is utilized to summarize meetings. For encoding meetings, a structural-level and semantic-level embedding strategy is considered, concretely, the Knowledge Graph is embedded to obtain the structural information, an interaction intention recognition model and a two-level transformer mechanism are devised to get the semantic information. Finally, the structural information and semantic information are combined and fed into the decoding network to generate meeting summaries. Extensive experiments on the Chinese meeting dataset show that our summarization framework outperforms other state-of-the-art models.

As the market for indoor spatial information burgeons, the construction of indoor spatial databases consequently gain attention. Since floorplans are portable records of buildings, they are an indispensable source for the efficient construction of indoor environments. However, as previous research on floorplan information retrieval usually targeted specific formats, a system for constructing spatial information must include heuristic refinement steps. This study aims to convert diverse floorplans into an integrated format using the style transfer by deep networks. Our deep networks mimic a robust perception of human that recognize the cell structure of floorplans under various formats. The integrated format ensures that unified post-processing steps are required to the vectorization of floorplans. Through this process, indoor spatial information is constructed in a pragmatic way, using a plethora of architectural floorplans.