Publication Date: 2019-10-23

Approval Date: 2019-09-13

Submission Date: 2019-08-16

Reference number of this document: OGC 18-089

Reference URL for this document:

Category: OGC Public Engineering Report

Editor: Charles Chen (Skymantics, LLC)

Title: OGC Indoor Mapping and Navigation Pilot Engineering Report

OGC Public Engineering Report


Copyright © 2019 Open Geospatial Consortium. To obtain additional rights of use, visit


This document is not an OGC Standard. This document is an OGC Public Engineering Report created as a deliverable in an OGC Interoperability Initiative and is not an official position of the OGC membership. It is distributed for review and comment. It is subject to change without notice and may not be referred to as an OGC Standard. Further, any OGC Public Engineering Report should not be referenced as required or mandatory technology in procurements. However, the discussions in this document could very well lead to the definition of an OGC Standard.


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Table of Contents

1. Executive Summary

The OGC Indoor Mapping and Navigation Pilot Initiative was sponsored by the National Institute of Standards and Technology (NIST) Public Safety Communications Research (PSCR) Division. This initiative addressed key challenges related to indoor mapping and navigation for the purpose of supporting first responders in fields such as fire-fighting. The focus of this initiative was on developing the capabilities and workflows required for pre-planning operations. This included scanning each building to produce a point cloud dataset and converting this source data into various intermediate forms to support the generation of indoor navigation routes. This Engineering Report (ER) describes the work conducted in this initiative, the lessons learned captured by participants, and future recommendations to support the public safety efforts and interoperability of the standards. It is expected that future OGC initiatives will address the real-time, event-driven aspects of indoor mapping and navigation for first response situations.

First responders typically survey high-risk facilities in their jurisdiction at least once per year as part of a pre-planning process. Pre-planning outputs are often in the form of reports, and first responders may generate their own hand-drawn maps during the process or annotate available floor plans (e.g., from computer-aided design models). Pre-planning is time-consuming, inefficient, and inherently complex considering the information and level of detail that should or could be captured, the lack of automation, and the difficulty identifying notable changes to facilities and infrastructure during successive pre-planning surveys.

Mobile three-dimensional (3D) Light Detection and Ranging (LiDAR) has been identified as a potentially transformational technology for first responders. Using LiDAR and 360-degree camera imagery, coupled with advanced software processing, first responders could efficiently capture 3D point clouds and a wealth of other information, both observed and derived, while walking through buildings as part of routine pre-planning operations. The use of 3D LiDAR and imagery has many potential upsides beyond just creating point clouds for visualization and mapping (e.g., use in localization, object classification, integration with virtual/augmented reality solutions, change detection, etc.).

Requirements and Research Motivation

The primary motivation for addressing the topic of indoor-mapping is based on a real-world scenario where public-safety first-responders such as firefighters are attempting to navigate a building to rescue a civilian or fellow downed firefighter. The scenario is focused on operational pre-planning using LiDAR based point cloud scans of buildings which may be used to map navigational routes around hazards and risks. To accelerate research and development for this public-safety-driven scenario, the requirements of this initiative were to conduct the following prototyping and demonstration activities:

  • Create and convert 3D indoor LiDAR point cloud models and associated imagery to functional building and navigation models.

  • Generate, store, and serve point cloud, building, and navigation models for visualization and navigation.

  • Derive dynamic turn-by-turn indoor navigation instructions based on the navigation model.

  • View and annotate point cloud data, imagery, and building models, along with navigation routes and instructions into, through, and out of buildings.

  • Capture and annotation of Public Safety features through buildings based on scans or images and including the public safety features as part of a Public Safety Extension.

Further, this initiative supported the improved interoperability of location-based technologies for indoor mapping and navigation through the use of OGC web services and OGC standards such as City Geography Markup Language (CityGML) and IndoorGML. The benefits from this initiative include the following:

Derived Benefits

  • Benefits to end users

    • First responders benefit from new tools and applications for improved awareness

    • Emergency evacuation processes can be expedited through better tools

    • Demonstration videos provide better community outreach for educational purposes

  • Benefits to SWG/DWG

    • Working groups may learn about the nuances in the use of point cloud data and associated standard data models

    • New insights into existing gaps in IndoorGML and CityGML for interoperability and real-world applications

    • Development of new algorithms or applications for IndoorGML navigation applications

  • Benefits to Developers

    • Ensure standards are truly interoperable for future standards and releases

    • Discovery of possible improvements to OGC standards and lessons learned

    • Lessons learned can assist with future research and reduce the learning curve

Key Findings

This pilot was successful in demonstrating 1) transformations of Point Clouds to CityGML with Public Safety Application Domain Extension (ADE), 2) transformations of CityGML into IndoorGML with Public Safety Extension, and 3) visualizations of the Public Safety data in a Pre-planning Tool Client using open standards and basic navigation capabilities. The participants met several challenges regarding data quality in the provided point cloud data, different interpretations of standards which caused errors and anomalies, and limited toolsets for manipulating CityGML and IndoorGML data. Many of these challenges are described within the individual ER sections and also in the final conclusions of the demonstration (see Chapter 11, Public Safety Scenario (Demonstration)).

Through the efforts of this pilot initiative, the following key findings have been determined:

  • Scanning was just the first step. It is critical that point cloud data scans are conducted thoroughly. Every effort should be made to ensure every unlocked door is opened and every locked door is annotated, and the resulting point cloud should be generated with Red-Green-Blue (RGB) data, not just grayscale, to ensure the best results from automation tools. Additional data cleansing of the point cloud data will still be required due to inherent building physics such as reflective surfaces and transparent glass. Automated conversions from one data form to another rarely produced a 100% complete result. A substantial amount of manual effort was required to repair the resulting output and fill gaps such as missing walls and doors. Once data has been cleansed and transformed to CityGML, manual annotation of public safety features is necessary. It is anticipated that future improvements in tools and software algorithms can begin to reliably automate some of these tasks.

  • CityGML Public Safety ADE was created as an ontology for first-responders. IndoorGML Core did not provide all requirements for Public Safety applications, so the IndoorGML Public Safety Extension was created to support the Public Safety contextual items.

  • If possible, all data should be georeferenced. If not, the data needs to be checked and manually repositioned to ensure the resulting data models are overlaid correctly. Misalignment due to improper georeference can result in improper subspacing due to rotation, incorrect network grid modeling, and navigation routing anomalies (e.g., zigzag lines).

  • Current editing tools for CityGML and IndoorGML are not sufficient enough to support editing functionality, and so use of other tools such as Revit (an IFC Editor) or TICA (an open source CityGML editor), to process point cloud data was necessary. It was found that conversion from point cloud to IFC first, then to CityGML simplified the process due to the better editing tools.

  • It remains an open question regarding how to best present navigation options to the end user. Higher density indoor subspacing network models allow for more precise navigation but clutter up the user’s view. Two methods are documented in this ER regarding room space calculations using centroid of rooms or grid-based subspacing. The goal is to achieve turn-by-turn directions, but the best result depends on the building and some combination of methods.

  • Some differences in GML 3.1.1 used by CityGML and GML 3.2.1 used by IndoorGML caused some issues. Future versions should consider harmonization of the standards.

Recommendations for Future Work

Future work items are documented in Section 11.4.2, “Recommendations for Future Work”. In summary, the recommendations for future work are as follows:

  • In terms of public safety, consideration should be made for both Indoor and Outdoor mapping as well as utilizing road and water networks and other accessibility

  • The National Alliance for Public Safety GIS Foundation (NAPSG) provides the public safety symbology, but CityGML and IndoorGML are semantic models that are not well-suited for symbology. The Styled Layer Descriptor (SLD) and Symbology Encoding (SE) standards could be improved beyond 2D into 3D to support this use case.

  • A large extent of this initiative involved data conversions into data models. For navigation, modeling must be extremely clean, whereas validation checks and business rules should be created to ensure the data is properly converted and suitable for use in calculating navigable routes.

  • Closer coordination between CityGML and IndoorGML is needed to ensure interoperability. This includes the GML difference, harmonized public safety ADE/Extension, and use of common building/space/room IDs.

  • Development of web and mobile clients could support public safety user applications as well as consideration for JSON, GeoJSON, and OpenAPIs.

  • Augmented Reality could provide significant benefits to public safety use cases, and use of 3D models used in this initiative can support these efforts.

1.1. Document contributor contact points

All questions regarding this document should be directed to the editor or the contributors:


Name Organization Role

Charles Chen



Dean Hintz

Safe Software


Ki-Joune Li

Pusan National University


Jason MacDonald



Ken Geange



Mohsen Kalantari



Mike Cross



Abdoulaye Diakite

University of New South Wales


Chih-Wei Kuan (Will)

Feng Chia University


1.2. Foreword

Attention is drawn to the possibility that some of the elements of this document may be the subject of patent rights. The Open Geospatial Consortium shall not be held responsible for identifying any or all such patent rights.

Recipients of this document are requested to submit, with their comments, notification of any relevant patent claims or other intellectual property rights of which they may be aware that might be infringed by any implementation of the standard set forth in this document, and to provide supporting documentation.

2. References

3. Terms and definitions

For the purposes of this report, the definitions specified in Clause 4 of the OWS Common Implementation Standard OGC 06-121r9 shall apply. In addition, the following terms and definitions apply.

  • Light Detection and Ranging (LiDAR) is a surveying method that uses laser light to illuminate an object or space and measure reflected light with a sensor. The variations in laser reflection and wavelengths is then used to generate a 3-dimensional representation of the target.

3.1. Abbreviated terms

  • 3D 3-Dimensional

  • 3DPS Three-Dimensional Portrayal Service

  • ADE Application Domain Extension

  • AEC Architecture, Engineering, and Construction

  • AR Augmented Reality

  • BIM Building Information Modeling

  • CFP Call for Participation

  • CGAL Computational Geometry Algorithm Library

  • CLI Command Line Interface

  • CSW Catalog Service for Web

  • CSW-T Transactional Catalog Service for Web

  • DDIL Denied, Degraded, Intermittent, or Limited

  • DWG Domain Working Group

  • EPSG European Petroleum Survey Group

  • ER Engineering Report

  • ETL Extract Transform Load

  • FME Feature Manipulation Engine (Safe Software)

  • FTP File Transfer Protocol

  • GML Geography Markup Language

  • IFC Industry Foundation Class

  • ISO International Organization for Standardization

  • IMDF Indoor Mapping Data Format (Apple Inc.)

  • LAZ LiDAR data file extension (.laz)

  • LCC Linear Cell Complex

  • LiDAR Light Detection and Ranging

  • MBB Minimum Bounding Box

  • NAPSG National Alliance for Public Safety GIS

  • NIST National Institute of Standards and Technology

  • NRG Node Relation Graph

  • OGC Open Geospatial Consortium

  • ORM OGC Reference Model

  • OWS OGC Web Services

  • PC Point Cloud

  • PCD Point Cloud Data

  • PS Public Safety

  • PSCR (NIST) Public Safety Communications Research Division

  • PSX Public Safety Extension

  • RGB Red Green Blue

  • RM-ODP Reference Model for Open Distributed Processing

  • SWG Standards Working Group

  • WFS-T Transactional Web Feature Service

  • WGS84 World Geodetic System 1984

  • WPS Web Processing Service

  • WG Working Group (SWG or DWG)

4. Overview

The Indoor Mapping and Navigation Pilot is an OGC Innovation Program initiative that addresses key challenges related to indoor mapping and navigation for first responders. The focus is on developing capabilities and workflows required to support pre-planning operations. These first responders periodically survey high-risk facilities as part of a pre-planning process and formulate reports which require floor plans and maps. Considerations are made to simplify this process to improve the efficiency, reduce complexity, and capture the necessary details through use of automation and tools.

This overview provides the following viewpoints according to the OGC Reference Model (ORM) which provides an architecture framework for the ongoing work of the OGC. The structure of the ORM is based on the Reference Model for Open Distributed Processing (RM-ODP).

4.1. Enterprise Viewpoint

The concept in this initiative is to take advantage of currently available LiDAR technology and camera imagery to capture a building as a set of 3D point cloud data during routing pre-planning operations, and then transform the data into usable formats for visualization and mapping. These tools already exist for the architecture, engineering, and construction (AEC) community, and it is expected that future investments will significantly lower the costs of tools such that it will become a cost-effective approach for public safety, building managers, and other industries.

In order to demonstrate this public-safety driven scenario, the following activities were conducted in this initiative:

  • Create and convert 3D indoor LiDAR point cloud models and associated imagery into functional building and navigation models.

  • Store and serve point cloud, building, and navigation models for visualization and navigation.

  • Derive dynamic indoor routes instructions based on the navigation model.

  • Enrich and annotate building models and navigation models, along with navigation routes with public safety features to help guide first responders in pre-planning activities.

4.2. Information Viewpoint

The Information Viewpoint considers the information models and encodings that will make up the content of the services and exchanges to be extended or developed to support this initiative. The following technical service components, data exchanges, and data model extension were developed by the initiative participants and demonstrated in this initiative:

  • Building Data - The building data consists of captured 3D point cloud data of buildings

  • Public Safety Features CityGML Application Domain Extension (ADE) - An XML extension of the CityGML standard to annotate features with public safety specific metadata and descriptions.

  • Building Modeler Service I (2 instances) - A web processing service that converts point cloud data into CityGML format.

  • Navigation Modeler Service I (3 instances) - A web processing service (WPS) that converts CityGML format data into IndoorGML format.

  • Building Model Repositories (3 variations) - A data storage and access service (Catalog) that provides the capability to store and retrieve data.

  • Indoor Navigation Service I (2 instances) - A web processing service that calculated an indoor navigable route using the IndoorGML data.

  • Pre-planning Tool Client I (2 instances) - A user interface client to interact with the various services and data components in a public safety scenario.

Figure 1 below shows a tiered technical viewpoint of the components in this pilot. Each tier is comprised of various components which access each other through open standards and interfaces to demonstrate interoperability. The Access Tier represents those components which include the data and the component services required to store and access the data. The Business Process Tier represents the components which provide data conversion into other data formats. The Client Tier represents the pre-planning clients which provide a user interface to interact with the components.

4 Overview 44edd
Figure 1. Component Architecture

4.3. Computational Viewpoint

The Computational Viewpoint is concerned with the functional decomposition of the system into a set of objects that interact at interfaces – enabling system distribution. The interface architecture for the Indoor Mapping and Navigation Pilot initiative is derived from the functional architecture provided in the CFP with some key changes. The architecture, as described by the Indoor Mapping and Navigation CFP, places a focus on the use of OGC web services for implementation of the modeler services as seen in Figure 2.

4 Overview b7b4f
Figure 2. CFP Component Architecture

During the kick-off discussions, it was determined that the initial viewpoint of the Building Modeler and Navigation Modeler fit well in the paradigm for WPS 2.0. However, it was determined that the interactions between the Pre-planning Tool Client and the Indoor Navigation Service is is better implemented using WPS instead of WFS. Additionally, the WFS-T originally envisioned for the Building Model Repository was replaced with a CSW-T interface. All components interface directly with the Building Model Repository via the CSW-T interface, retrieving necessary data and storing the resulting transformed data. The clients access the CSW-T to retrieve each data set to display in the client user interface and generate indoor routes for navigation. Figure 3 shows the updated component architecture after discussion at the Indoor Pilot Kick-off meeting.

4 overview 37909
Figure 3. Updated Component Architecture

It should be noted that the Navigation Modeler service was implemented in two different ways: 1) converting the CityGML output of the Building Modeler into IndoorGML, and 2) reversely, converting point cloud data directly into IndoorGML and then back into CityGML. The reason for this is due to the fact that one participant focused on the indoor spatial mapping from point cloud data rather than from a top-down building geometry to indoor space workflow. More details can be found in Chapter 7, Navigation Modeler Service.

4.4. Engineering Viewpoint

The Engineering Viewpoint is concerned with the infrastructure required to support system distribution. It focuses on the mechanisms and functions required to support distributed interaction between objects in the system, and hides the complexities of those interactions. It exposes the distributed nature of the system, describing the infrastructure, mechanisms and functions for object distribution, distribution transparency and constraints, bindings and interactions.

The following sequence diagram describes the conceptual flow for the Pilot architecture.

4 overview 0110c
Figure 4. Indoor Mapping & Navigation Pilot - Conceptual Flow

4.5. Technology Viewpoint

Each of the following chapters in this ER describe the individual Technology Viewpoint of the various components, how they are developed, the software tools utilized, and the distribution of components needed to achieve the result of the Technology Integration Experiments (TIEs) conducted in this initiative.

Chapter 5, Data Sources describes the various data sets provided in this pilot. The datasets include point clouds, CityGML, and IndoorGML data. This chapter also catalogs the provided sample data, the official demonstration data, data derived and converted from the original formats, and details the data cleansing and enrichment. Some data is enriched with Public Safety Features CityGML ADE, which is documented in a separate ER. For more information, refer to the OGC 19-032 CityGML Public Safety ADE Engineering Report.

Chapter 6, Building Modeler Service describes the Building Modeler Service which converts point cloud data into CityGML data. This task was to create a building modeler application that can convert point cloud models and associated images (such as those generated by the Building Data) into semantic 3D building models compliant with the most recent or stable version of CityGML (2.0). Models generated by this component are notated with Public Safety Features from the CityGML ADE. The building modeler may be developed as a web processing service (WPS) compliant with the OGC WPS 2.0 Interface Standard (OGC14-065), although this was not a strict requirement.

Chapter 7, Navigation Modeler Service describes the Navigation Modeler Service which converts CityGML data into IndoorGML data. This task was to create a navigation modeler application that can convert output from the Building Modeler Service to IndoorGML usable for indoor navigation. The Navigation Modeler also generated navigation network information to support route calculations by the Indoor Navigation Service. The navigation modeler may be developed as a WPS compliant service, although this was not a strict requirement.

Chapter 8, Building Model Repository describes the Building Model Repository which stores the various datasets and makes them available to other services and end users. This task was to create a building model repository to store and serve point cloud, building, and navigation models (see Building Data, Building Modeler Service, and Navigation Modeler Service). The repository exposes a model catalog and provides authenticated access. The repository is interoperable with application clients.

Chapter 9, Indoor Navigation Service describes the Indoor Navigation Service which consumes the IndoorGML data to produce a navigation route based on parametric inputs. This task involved creating an indoor navigation service that can derive ‘turn-by-turn’ instructions between any two points in a building, including exits, based on network models created by the Navigation Modeler Service and stored in the IndoorGML. The service used navigation algorithms, not necessarily developed in this Initiative, which were optimized for specific criteria (e.g. routes optimized based on time, distance, or risk) requested by the Pre-planning Tool Client.

Chapter 10, Pre-planning Tool Client describes the Pre-planning Tool Client for end user access to the services and dataset renderings for mapping and navigation display. This task was to create a visualization client application for users to request and view point cloud data and building models from the Building Model Repository, as well as navigation instructions and routes from the Indoor Navigation Service. The client is a graphical user interface that enables public safety personnel to view and annotate models captured during pre-planning. The client should seamlessly transition between 2D and 3D views and should allow users to visualize hypothetical routes into, through, and out of buildings along with the appropriate metrics (e.g. estimated time, distance, risk, etc.) based on additional input parameters considered by the navigation service. The client should use the OGC WFS Interface Standard and OGC 3DPS to request and receive models, scenes, and services.

5. Data Sources

This chapter describes the point cloud source data provided by the sponsor (Section 5.3, “Sponsor Provided Data”) and participants (Section 5.2, “Participant Provided Data”). It also describes the data derived from these sources into the CityGML, IndoorGML, and public safety extensions.

5.1. Types of Data

This Pilot uses several data sources for testing and experimentation including Point Cloud data, CityGML data, and IndoorGML data. CityGML is an open standardized data model and exchange format to store digital 3D models of cities and landscapes. The data files are hosted by the OGC in a secure File Transfer Protocol (FTP) server accessible to participants. Enriched data includes cleansed data which is then enriched with an Application Domain Extension (ADE), a built-in mechanism of CityGML to augment its data model with additional concepts required by particular use cases. The ADE is a mechanism for enriching the data model with new feature classes and attributes, while preserving the semantic structure of CityGML. IndoorGML is an OGC standard for an open data model and XML schema for indoor spatial information. IndoorGML does not officially have an extension mechanism, but one was needed to carry the Public Safety features that were forwarded from the CityGML Public Safety ADE.

5.1.1. Point Cloud Data

The point cloud data used in this pilot is generated through LiDAR scans of various buildings. Another concurrent project was developing LiDAR building scans, and as a result the data contribution (Section 5.3, “Sponsor Provided Data”) was delayed until later in the pilot. Therefore, sample data (Section 5.2, “Participant Provided Data”) provided by the participants was used for the beginning of the development phase. Test data provided by Pusan National University (PNU) was used for initial development and testing. Later, efforts transitioned to official data provided by the sponsor.

5.1.2. CityGML Data

CityGML 2.0 was used to represent the building geometries including walls, floors, ceilings, doors, windows, furniture, etc. In order to begin development and testing earlier in the pilot, CityGML was generated from Victoria Airport’s publicly available sample dataset in Apple IMDF using FME from Safe Software. The data was later enriched with Public Safety ADE. Pusan National University generated IndoorGML from point cloud data using a partial manual process with an open source software application, and then converted IndoorGML to CityGML. Other participants’ early attempts to generate CityGML datasets from point clouds resulted in CityGML with a few large mesh features that lacked structure and were not readily convertible to IndoorGML.

One workflow that proved productive was to do automated conversion of point cloud to IFC data, and then use an editing tool (Revit) to structure and enrich the data for use in IndoorGML. A key part of this process was room generation. FME was used to convert and simplify the IFC to CityGML in a mostly automated process with some minor configurations to account for dataset variations.

5.1.3. IndoorGML Data

IndoorGML data describes the indoor spaces of a building. Spaces are divided into navigable and non-navigable spaces. The navigable spaces are linked to generate a navigational network, which can then be traversed to generate navigable routes. IndoorGML does not officially have an extension mechanism, but the participants collaborated to develop an IndoorGML Public Safety Extension to carry the public safety features that were forwarded from the CityGML Public Safety ADE. This functionally is being proposed to the OGC IndoorGML SWG for consideration and is planned to be used for the new upcoming IndoorGML 2.0 work.

5.2. Participant Provided Data

The following sample data was provided by the participants for testing and evaluation as components were being developed. This was done to ensure parallel development to achieve the TIEs later in the project while participants were waiting on the demonstration data.

5.2.1. Point Cloud - Korea University Central Plaza

This data contains a LiDAR point cloud scan of Korea University’s Central Plaza, an underground plaza at the Anam Science Field Campus. The author, TeeLabs Co., is a collaborative work partner with Pusan National University and supplied the following point cloud data for use in this pilot initiative. The data contains a large point cloud with more than 100 million points and contains images with camera pose data. Most of the noise in the data has been cleansed, and each object is individually separated. The data was cleansed using Cloud Compare, an open source point cloud viewer.

  1. Name: KU-Central-Plaza

  2. Source: 3D LiDAR x 2EA, 360 Camera, IMU

  3. Editing: Cleansing (PCD Noise Removal) using a semi-automatic process

  4. Author: TeeLabs, Co. LTD.