Accessible path finding for historic urban environments: feature extraction and vectorization from point clouds

Sidewalk inventory is a topic whose importance is increasing together with the widespread use of smart city management. In order to manage the city properly and to make informed decisions, it is necessary to know the real conditions of the city. Furthermore, when planning and calculating cultural routes within the city, these routes must take into account the specific needs of all users. Therefore, it is important to know the conditions of the city’s sidewalk network and also their physical and geometrical characteristics. Typically, sidewalk network are generated basing on existing cartographic data, and sidewalk attributes are gathered through crowdsourcing. In this paper, the sidewalk network of an historic city was produced starting from point cloud data. The point cloud was semantically segmented in ”roads” and ”sidewalks”, and then the cluster of points of sidewalks surfaces were used to compute sidewalk attributes and to generate a vector layer composed of nodes and edges. The vector layer was then used to compute accessible paths between Points of Interest, using QGIS. The tests made on a real case study, the historic city and UNESCO site of Sabbioneta (Italy), shows a vectorization accuracy of 98.7%. In future, the vector layers and the computed paths could be used to generate maps for city planners, and to develop web or mobile phones routing apps.Ministerio de Ciencia e Innovación | Ref. RYC2020-029193-

Towards automatic reconstruction of indoor scenes from incomplete point clouds: door and window detection and regularization

In the last years, point clouds have become the main source of information for building modelling. Although a considerable amount of methodologies addressing the automated generation of 3D models from point clouds have been developed, indoor modelling is still a challenging task due to complex building layouts and the high presence of severe clutters and occlusions. Most of methodologies are highly dependent on data quality, often producing irregular and non-consistent models. Although manmade environments generally exhibit some regularities, they are not commonly considered. This paper presents an optimization-based approach for detecting regularities (i.e., same shape, same alignment and same spacing) in building indoor features. The methodology starts from the detection of openings based on a voxel-based visibility analysis to distinguish ‘occluded’ from ‘empty’ regions in wall surfaces. The extraction of regular patterns in windows is addressed from studying the point cloud from an outdoor perspective. The layout is regularized by minimizing deformations while respecting the detected constraints. The methodology applies for elements placed in the same planeXunta de Galicia | Ref. ED481B 2016/079-

Integration of infrared thermography and photogrammetric surveying of built landscape

The thermal analysis of buildings represents a key-step for reduction of energy consumption, also in the case of Cultural Heritage. Here the complexity of the constructions and the adopted materials might require special analysis and tailored solutions. Infrared Thermography (IRT) is an important non-destructive investigation technique that may aid in the thermal analysis of buildings. The paper reports the application of IRT on a listed building, belonging to the Cultural Heritage and to a residential one, as a demonstration that IRT is a suitable and convenient tool for analysing the existing buildings. The purposes of the analysis are the assessment of the damages and energy efficiency of the building envelope. Since in many cases the complex geometry of historic constructions may involve the thermal analysis, the integration of IRT and accurate 3D models were developed during the latest years. Here authors propose a solution based on the up-to-date photogrammetric solutions for purely image-based 3D modelling, including automatic image orientation/sensor calibration using Structure-from-Motion and dense matching. Thus, an almost fully automatic pipeline for the generation of accurate 3D models showing the temperatures on a building skin in a realistic manner is described, where the only manual task is given by the measurement of a few common points for co-registration of RGB and IR photogrammetric projects.Xunta de Galicia | Ref. ED481B 2016/079-

Automatic extraction of a navigation graph intended for indoorgml from an indoor point cloud

Indoor environments tend to be more complex and more populated when buildings are accessible to the public. The need for knowing where people are, how they can get somewhere or how to reach them in these buildings is thus equally increasing. In this research point clouds are used, obtained by dynamic laser scanning of a building, since we cannot rely on architectural drawings for maps and paths, which can be outdated. The presented method focuses on the creation of an indoor navigation graph, based on IndoorGML structure, in a fast and automated way, while retaining the type of walkable surface. In this paper the focus has been on door detection, because doors are essential elements in an indoor environment, seeing that they connect spaces and are a logical step in a route. This paper describes a way to detect doors using 3D Medial Axis Transform (MAT) combined with the intelligence stored in the path of a mobile laser scanner, showing good first results. Additionally different spaces (e.g. rooms and corridors) in the building are identified and slopes and stairs in walkable spaces are detected. This results in a navigation graph which can be stored in an IndoorGML structure

Point clouds to direct indoor pedestrian pathfinding

Increase in building complexity can cause difficulties orienting people, especially people with reduced mobility. This work presents a methodology to enable the direct use of indoor point clouds as navigable models for pathfinding. Input point cloud is classified in horizontal and vertical elements according to inclination of each point respect to n neighbour points. Points belonging to the main floor are detected by histogram application. Other floors at different heights and stairs are detected by analysing the proximity to the detected main floor. Then, point cloud regions classified as floor are rasterized to delimit navigable surface and occlusions are corrected by applying morphological operations assuming planarity and taking into account the existence of obstacles. Finally, point cloud of navigable floor is downsampled and structured in a grid. Remaining points are nodes to create navigable indoor graph. The methodology has been tested in two real case studies provided by the ISPRS benchmark on indoor modelling. A pathfinding algorithm is applied to generate routes and to verify the usability of generated graphs. Generated models and routes are coherent with selected motor skills because routes avoid obstacles and can cross areas of non-acquired data. The proposed methodology allows to use point clouds directly as navigation graphs, without an intermediate phase of generating parametric model of surfacesUniversidade de Vigo | Ref. 00VI 131H 641.02Xunta de Galicia | Ref. ED481B 2016/079-0Xunta de Galicia | Ref. ED431C 2016-038Ministerio de Economía, Industria y Competitividad | Ref. TIN2016-77158-C4-2-RMinisterio de Economía, Industria y Competitividad | Ref. RTC-2016-5257-

Scan planning and route optimization for control of execution of as-designed BIM

Abstract. Scan-to-BIM systems have been recently proposed for the dimensional and quality assessment of as-built construction components with planned works. The procedure is generally based on the geometric alignment and comparison of as-built laser scans with as-designed BIM models. A major concern in Scan-to-BIM procedures is point cloud quality in terms of data completeness and consequently, the scanning process should be designed in order to obtain a full coverage of the scene while avoiding major occlusions. This work proposes a method to optimize the number and scan positions for Scan-to-BIM procedures following stop & go scanning. The method is based on a visibility analysis using a ray-tracing algorithm. In addition, the optimal route between scan positions is formulated as a travelling salesman problem and solved using a suboptimal ant colony optimization algorithm. The distribution of candidate positions follows a grid-based structure, although other distributions based on triangulation or tessellation can be implemented to reduce the number of candidate positions and processing time.Xunta de Galicia | Ref. ED481B 2016/079-0Xunta de Galicia | Ref. ED431C 2016- 038Ministerio de Economía, Industria y Competitividad | Ref. TIN2016-77158- C4-2-RMinisterio de Economia, Industria y Competitividad | Ref. RTC-2016-5257-

A deep learning approach for the recognition of urban ground pavements in historical sites

Urban management is a topic of great interest for local administrators, particularly because it is strongly connected to smart city issues and can have a great impact on making cities more sustainable. In particular, thinking about the management of the physical accessibility of cities, the possibility of automating data collection in urban areas is of great interest. Focusing then on historical centres and urban areas of cities and historical sites, it can be noted that their ground surfaces are generally characterised by the use of a multitude of different pavements. To strengthen the management of such urban areas, a comprehensive mapping of the different pavements can be very useful. In this paper, the survey of a historical city (Sabbioneta, in northern Italy) carried out with a Mobile Mapping System (MMS) was used as a starting point. The approach here presented exploit Deep Learning (DL) to classify the different pavings. Firstly, the points belonging to the ground surfaces of the point cloud were selected and the point cloud was rasterised. Then the raster images were used to perform a material classification using the Deep Learning approach, implementing U-Net coupled with ResNet 18. Five different classes of materials were identified, namely sampietrini, bricks, cobblestone, stone, asphalt. The average accuracy of the result is 94%.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Ministerio de Ciencia e Innovación | Ref. PID2019-105221RB-C43Ministerio de Ciencia e Innovación | Ref. RYC2020-029193-

An evaluation framework for benchmarking indoor modelling methods

Despite recent progress in the development of methods for automated reconstruction of indoor models, a comparative performance evaluation of these methods is not available due to the lack of publicly available benchmark datasets and a common evaluation framework. The ISPRS Benchmark on Indoor Modelling is an effort to enable comparison and benchmarking of indoor modelling methods by providing a benchmark dataset and a comprehensive evaluation framework. In this paper, we propose a framework for the evaluation of indoor modelling methods, and discuss various quality aspects of the reconstruction methods as well as the reconstructed models. We discuss the challenges in quantitative quality evaluation of indoor models through comparison with a reference model, and propose suitable measures and methods for comparing an automatically reconstructed indoor model with a reference.Xunta de Galicia | Ref. ED481B 2016/079-

Scan planning optimization for outdoor archaeological sites

The protection and management of archaeological sites require from a deep documentation and analysis, and although hand measuring and documentation is the cheapest way for collecting data, laser scanner has been gradually integrated for the geometrical data capture since point clouds have a high quality in terms of accuracy, precision and resolution. Although acquisition with laser scanner is considered a quick process, scan planning is of high relevance when considering outdoor archaeological sites because of their large size and complexity. In this paper, an automatic methodology to optimize the number and position of scans in order to obtain a point cloud of high quality in terms of data completeness is proposed. The aim of the methodology is to minimize the number of scans, minimizing at the same time the estimated surveying time and the amount of repetitive acquired data. Scan candidates are generated by using a grid-based and a triangulation-based distribution, and results show a faster analysis when triangulation is implemented. The methodology is tested into two real case studies from Italy and Spain, showing the applicability of scan planning in archaeological sitesXunta de Galicia | Ref. ED481B 2016/079-0Xunta de Galicia | Ref. ED431C 2016-038Universidade de Vigo | Ref. 00VI 131H 641.02Ministerio de Economía, Industria y Competitividad | Ref. TIN2016-77158-C4-2-RMinisterio de Economía, Industria y Competitividad | Ref. RTC-2016-5257-7European Cooperation in Science and Technology (COST) | Ref. CA1520

3D mapping of indoor and outdoor environments using apple smart devices

Recent integration of LiDAR into smartphones opens up a whole new world of possibilities for 3D indoor/outdoor mapping. Although these new systems offer an unprecedent opportunity for the democratization in the use of scanning technology, data quality is lower than data captured from high-end LiDAR sensors. This paper is focused on discussing the capability of recent Apple smart devices for applications related with 3D mapping of indoor and outdoor environments. Indoor scenes are evaluated from a reconstruction perspective, and three geometric aspects (local precision, global correctness, and surface coverage) are considered using data captured in two adjacent rooms. Outdoor environments are analysed from a mobility point of view, and elements defining the physical accessibility in building entrances are considered for evaluation.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Ministerio de Ciencia e Innovación | Ref. PID2019-105221RB-C43Ministerio de Ciencia e Innovación | Ref. RYC2020-029193-