A set of 3D digital maps show the interior structure of a building or a venue. It includes floors, units, place-of-interest and other indoor map features which supports indoor navigation, indoor spatial analysis, etc.

Indoor Viewer is an ArcGIS Indoors application template for viewing indoor maps and various other indoor data. This app provides an indoor mapping experience for searching and exploring locations of people, places, and events happening within your workplace. You can use Indoor Viewer to confidently navigate your workplace or campus, increase productivity and collaboration with your colleagues, and feel more connected to your workplace or campus.Key Features offered by the Indoor Viewer app:Explore indoor maps and navigate to people and places within your organization.Search for specific people, activities, events, offices, classrooms, and other points of interest.Integrate with your calendar to see locations of scheduled meetings and eventsBook office hotels or meeting rooms for collaboration.Review estimated travel times to offices, meeting rooms and event locations.@@indoors_features6@@Share indoor location to update others with your current location or help others find a location.Link to other apps and pass indoor location information such as when submitting work requests to maintenance systems.Indoor Viewer application requires Indoors Maps license.

especially about the radio channel

With the White House release of guidelines for states to reopen and employees to gradually return to work, facilities are tasked with complex challenges. Managers must make decisions to ensure a safe work environment and adhere to social distancing requirements. Office layouts must be restructured for adequate spacing between workspaces and to allow for routing that minimizes close-proximity encounters. Clear communication with staff will also be a key factor: Which areas should be avoided? When has an area last be cleaned?The ArcGIS Indoors system from Esri can help answer these geospatially focused questions for reopening the workplace. With indoor maps and an indoor positioning system, managers can create a floor-plan level awareness of the workplace, one that will allow for safe reopening._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Indoor Mapping Solution Market Outlook

The global indoor mapping solution market size is projected to grow from USD 3.1 billion in 2023 to an estimated USD 8.7 billion by 2032, at a compound annual growth rate (CAGR) of 12.1% during the forecast period. The market growth is driven by the increasing adoption of advanced technologies such as AI and IoT, which are enhancing the capabilities of indoor mapping solutions across various industries.

One of the primary growth factors for the indoor mapping solution market is the rising demand for enhanced navigation and wayfinding solutions within large and complex indoor environments like malls, airports, and hospitals. These solutions not only assist visitors in finding their way but also offer businesses valuable analytics to improve customer experience and operational efficiency. Furthermore, the integration of IoT devices and AI technologies is significantly improving the accuracy and functionality of indoor mapping solutions, making them indispensable tools for facility management and asset tracking.

Another significant growth driver is the growing need for efficient facility management systems in corporate offices, manufacturing plants, and warehouses. Indoor mapping solutions enable facility managers to visualize spaces, optimize layouts, and manage assets more effectively. This increased efficiency leads to cost savings and enhanced productivity, making these solutions highly attractive to businesses across diverse sectors. Additionally, the surge in smart building initiatives is further bolstering the demand for indoor mapping solutions as they play a crucial role in the implementation and management of intelligent building systems.

The retail sector is also contributing to the market's expansion as retailers increasingly adopt indoor mapping solutions to enhance customer experiences. These solutions facilitate personalized in-store navigation, promotional offers, and seamless integration with mobile applications, thereby boosting customer engagement and satisfaction. The healthcare sector is another significant contributor, utilizing indoor mapping for efficient patient navigation, asset tracking, and emergency response planning, which are critical for improving healthcare delivery and operational workflows.

Indoor Positioning And Navigations are becoming increasingly vital in the realm of indoor mapping solutions. As large venues such as airports, shopping centers, and hospitals continue to expand, the need for precise indoor navigation systems becomes more pronounced. These systems utilize a combination of technologies like Bluetooth beacons, Wi-Fi triangulation, and even augmented reality to guide users through complex indoor spaces. The integration of Indoor Positioning And Navigations with existing mapping solutions not only enhances user experience but also provides businesses with valuable data insights. This data can be leveraged to optimize space utilization, improve safety protocols, and enhance overall operational efficiency. As the technology continues to evolve, it is expected to play a crucial role in the future of smart buildings and urban planning.

Regionally, North America holds the dominant share of the indoor mapping solution market due to the high adoption rate of advanced technologies and the presence of leading market players. The regionÂ’s mature IT infrastructure and increased focus on enhancing user experiences in commercial spaces further drive the market. Europe follows closely, with significant investments in smart building projects and a growing emphasis on sustainable facility management solutions. Asia Pacific is expected to witness the highest growth rate, attributed to rapid urbanization, increased investments in smart city projects, and the rising adoption of digital solutions across various industries.

Component Analysis

The indoor mapping solution market is segmented by component into software and services. The software segment dominates the market due to the increasing demand for sophisticated mapping applications that offer high accuracy and advanced functionalities. Indoor mapping software includes various tools for visualization, analysis, and asset tracking, which are essential for industries like retail, healthcare, and transportation. The continuous advancements in software technology are further enhancing the capabilities of indoor mapping solutions, driving their adoption across different sectors.

Indoor Space Planner is an ArcGIS Indoors application template for space management and assignment inside buildings. It provides an indoor mapping experience to perform occupant assignment updates to account for reorganization efforts, changes to office space, or growth in the organization. You can use Space Planner to plan office moves as well as define areas and staff assignments for office hoteling.Key Features offered by the Indoor Space Planner appConsolidate space through occupant office assignments and make more space available for other usesUpdate occupant assignments with newly built or acquired office spacesDefine seating arrangements to support a safer work environmentIncrease collaboration between staff and teams with space assignment for improved productivity and communicationAssign staff and teams to areas that optimize access to building assets such as collaboration spaces, meeting rooms, equipment, or amenitiesSpace Planner application requires Indoors Spaces license.

The Indoor Mapping Solution market is rapidly evolving, transforming the way businesses navigate and utilize complex indoor spaces. From shopping malls and airports to hospitals and corporate offices, advanced indoor mapping technologies provide detailed navigation and orientation capabilities that enhance user expe

This is the first batch of WiFi RSS RTT datasets with LOS conditions we published. Please see https://doi.org/10.5281/zenodo.11558792 for the second batch.

Please do use version 2 for better quality.

We provide publicly available datasets of three different indoor scenarios: building floor, office and apartment. The datasets contain both WiFi RSS and RTT signal measures with groud truth coordinates label and LOS condition label.

1.Building Floor

This is a detailed WiFi RTT and RSS dataset of a whole floor of a university building, of moare than 92 x 15 square metres. We divided the area of interest was divided into discrete grids and labelled them with correct ground truth coordinates and the LoS APs from the grid. The dataset contains WiFi RTT and RSS signal measures recorded in 642 reference points for 3 days and is well separated so that training points and testing points will not overlap.

1. Office

Office scenario is of more than 4.5 x 5.5 square metres. 3 APs are set to cover the whole space. At least two LOS AP could be seen at any reference point (RP).

3.Apartment

Apartment scenario is of more than 7.7 x 9.4 square metres.Four APs were leveraged to generate WiFi signal measures for this testbed. Note that AP 1 in the apartment dataset was positioned so that it could had an NLOS path to most of the testbed.

Collection methodology

The APs utilised were Google WiFi Router AC-1304, the smartphone used to collect the data was Google Pixel 3 with Android 9.

The ground truth coordinates were collected using fixed tile size on the floor and manual post-it note markers.

Only RTT-enabled APs were included in the dataset.

The features of the datasets

The features of the building floor dataset are as follows:

Testbed area: 92 × 15 m2

Grid size: 0.6 × 0.6 m2

Number of AP: 13

Number of reference points: 642

Samples per reference point: 120

Number of all data samples: 77040

Number of training samples: 57960

Number of testing samples: 19080

Signal measure: WiFi RTT, WiFi RSS

Collection time interval: 3 days

The features of the office dataset are as follows:

Testbed area: 4.5 × 5.5 m2

Grid size: 0.455 × 0.455 m2

Number of AP: 3

Reference points: 37

Samples per reference point: 120

Data samples: 4,440

Training samples: 3,240

Testing samples: 1,200

Signal measure: WiFi RTT, WiFi RSS

Other information: LOS condition of every AP

Collection time: 1 day

Notes: A LOS scenario

The features of the apartment dataset are as follows:

Testbed area: 7.7 × 9.4 m2

Grid size: 0.48 × 0.48 m2

Number of AP: 4

Reference points: 110

Samples per reference point: 120

Data samples: 13,200

Training samples: 9,720

Testing samples: 3,480

Signal measure: WiFi RTT, WiFi RSS

Other information: LOS condition of every AP

Collection time: 1 day

Notes: Contains an AP with NLOS paths for most of the RPs

Dataset explanation

The columns of the dataset are as follows:

Column 'X': the X coordinates of the sample.

Column 'Y': the Y coordinates of the sample.

Column 'AP1 RTT(mm)', 'AP2 RTT(mm)', ..., 'AP13 RTT(mm)': the RTT measure from corresponding AP at a reference point.

Column 'AP1 RSS(dBm)', 'AP2 RSS(dBm)', ..., 'AP13 RSS(dBm)': the RSS measure from corresponding AP at a reference point.

Column 'LOS APs': indicating which AP has a LOS to this reference point.

Please note:

The RSS value -200 dBm indicates that the AP is too far away from the current reference point and no signals could be heard from it.

The RTT value 100,000 mm indicates that no signal is received from the specific AP.

Citation request

When using this dataset, please cite the following two items:Feng, X., Nguyen, K. A., & Luo, Z. (2024). WiFi RTT RSS dataset for indoor positioning [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11558192@article{feng2023wifi, title={WiFi round-trip time (RTT) fingerprinting: an analysis of the properties and the performance in non-line-of-sight environments}, author={Feng, Xu and Nguyen, Khuong an and Luo, Zhiyuan}, journal={Journal of Location Based Services}, volume={17}, number={4}, pages={307--339}, year={2023}, publisher={Taylor & Francis} }

Dataset of paper "Radio-based Sensing and Indoor Mapping with Millimeter-Wave 5G NR Signals" presented in International Conference on Localization and GNSS (ICL-GNSS) 2020.

The measurement data contains indoor mapping results using millimeter-wave 5G NR signals at 28 GHz. The measurement campaign was conducted at an indoor office environment in Hervanta Campus of Tampere University. Six different sets of measurements contain the range profiles after the proposed radar processing.

The file "indoorMapping_processing.m" shows how to process and plot the shared data.

This layer shows the 3D indoor map of some buildings' interior structures in Hong Kong. It is a set of the data made available by Lands Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.

This is the second batch of WiFi RSS RTT datasets with LOS conditions we published. Please see https://doi.org/10.5281/zenodo.11558192 for the first release.

We provide three real-world datasets for indoor positioning model selection purpose. We divided the area of interest was divided into discrete grids and labelled them with correct ground truth coordinates and the LoS APs from the grid. The dataset contains WiFi RTT and RSS signal measures and is well separated so that training points and testing points will not overlap. Please find the datasets in the 'data' folder. The datasets contain both WiFi RSS and RTT signal measures with groud truth coordinates label and LOS condition label.

Lecture theatre: This is a entirely LOS scenario with 5 APs. 60 scans of WiFi RTT and RSS signal measures were collected at each reference point (RP).

Corridor: This is a entirely NLOS scenario with 4 APs. 60 scans of WiFi RTT and RSS signal measures were collected at each reference point (RP).

Office: This is a mixed LOS-NLOS scenario with 5 APs. At least one AP was NLOS for each RP. 60 scans of WiFi RTT and RSS signal measures were collected at each reference point (RP).

Collection methodology

The APs utilised were Google WiFi Router AC-1304, the smartphone used to collect the data was Google Pixel 3 with Android 9.

The ground truth coordinates were collected using fixed tile size on the floor and manual post-it note markers.

Only RTT-enabled APs were included in the dataset.

The features of the dataset

The features of the lecture theatre dataset are as follows:

Testbed area: 15 × 14.5 m2 Grid size: 0.6 × 0.6 m2Number of AP: 5 Number of reference points: 120 Samples per reference point: 60 Number of all data samples: 7,200 Number of training samples: 5,400 Number of testing samples: 1,800 Signal measure: WiFi RTT, WiFi RSS Note: Entirely LOS

The features of the corricor dataset are as follows:

Testbed area: 35 × 6 m2 Grid size: 0.6 × 0.6 m2Number of AP: 4 Number of reference points: 114 Samples per reference point: 60 Number of all data samples: 6,840 Number of training samples: 5,130 Number of testing samples: 1,710 Signal measure: WiFi RTT, WiFi RSS Note: Miexed LOS-NLOS. At least one AP was NLOS for each RP.

The features of the office dataset are as follows:

Testbed area: 18 × 5.5 m2 Grid size: 0.6 × 0.6 m2Number of AP: 5 Number of reference points: 108 Samples per reference point: 60 Number of all data samples: 6,480 Number of training samples: 4,860 Number of testing samples: 1,620 Signal measure: WiFi RTT, WiFi RSS Note: Entirely NLOS

Dataset explanation

The columns of the dataset are as follows:

Column 'X': the X coordinates of the sample. Column 'Y': the Y coordinates of the sample. Column 'AP1 RTT(mm)', 'AP2 RTT(mm)', ..., 'AP5 RTT(mm)': the RTT measure from corresponding AP at a reference point. Column 'AP1 RSS(dBm)', 'AP2 RSS(dBm)', ..., 'AP5 RSS(dBm)': the RSS measure from corresponding AP at a reference point. Column 'LOS APs': indicating which AP has a LOS to this reference point.

Please note:

The RSS value -200 dBm indicates that the AP is too far away from the current reference point and no signals could be heard from it.

The RTT value 100,000 mm indicates that no signal is received from the specific AP.

Citation request

When using this dataset, please cite the following three items:

Feng, X., Nguyen, K. A., & Zhiyuan, L. (2024). WiFi RSS & RTT dataset with different LOS conditions for indoor positioning [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11558792

@article{feng2024wifi, title={A WiFi RSS-RTT indoor positioning system using dynamic model switching algorithm}, author={Feng, Xu and Nguyen, Khuong An and Luo, Zhiyuan}, journal={IEEE Journal of Indoor and Seamless Positioning and Navigation}, year={2024}, publisher={IEEE} }@inproceedings{feng2023dynamic, title={A dynamic model switching algorithm for WiFi fingerprinting indoor positioning}, author={Feng, Xu and Nguyen, Khuong An and Luo, Zhiyuan}, booktitle={2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, pages={1--6}, year={2023}, organization={IEEE} }

The Indoor Location by Positioning Systems (Indoor LBS) market is experiencing robust growth, projected to reach a market size of $3,803 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 21.1% from 2025 to 2033. This significant expansion is driven by the increasing demand for real-time location tracking and asset management across diverse sectors. The rising adoption of smart buildings, the need for enhanced security and safety measures in public spaces and industrial settings, and the growing importance of precise indoor navigation in healthcare and logistics are key factors fueling market growth. Technological advancements in Bluetooth-based and web-based positioning systems, offering improved accuracy and cost-effectiveness, further contribute to market expansion. Segmentation reveals strong demand across various applications, including office and commercial buildings, government and public safety, healthcare, aviation, and the oil, gas, and mining industries. Competition is intense, with key players such as Zebra Technologies, Aruba, and Esri vying for market share through innovation and strategic partnerships. Geographic analysis indicates strong growth potential across North America, Europe, and the Asia-Pacific region, driven by factors such as early adoption of technology and robust infrastructure development. The substantial growth trajectory of the Indoor LBS market is expected to continue throughout the forecast period (2025-2033), propelled by ongoing technological advancements and increased digital transformation across industries. The development of more sophisticated and integrated positioning systems, incorporating artificial intelligence and machine learning capabilities, will likely further enhance accuracy and functionality. The integration of Indoor LBS with other technologies, such as IoT and cloud computing, will also open up new possibilities for applications in areas like smart retail, supply chain optimization, and personalized customer experiences. However, challenges such as the high initial investment costs associated with system implementation and concerns regarding data privacy and security may act as potential restraints.

The Indoor Positioning and Tracking System (IPTS) market is experiencing robust growth, projected to reach $1389.1 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 8.9% from 2025 to 2033. This expansion is fueled by several key drivers. Increasing demand for enhanced security and asset management across various sectors, including malls, airports, and offices, is a primary factor. The rising adoption of Internet of Things (IoT) devices and the need for real-time location tracking for improved operational efficiency further contribute to market growth. Advancements in technologies like Bluetooth Low Energy (BLE), Ultra-Wideband (UWB), and Wi-Fi positioning are enabling more accurate and cost-effective solutions, driving wider adoption. Furthermore, the increasing need for contactless solutions and social distancing monitoring post-pandemic is accelerating the IPTS market's growth trajectory. The market is segmented by application (malls, airports, offices, stadiums, schools, others) and type (hardware, software), allowing for targeted solutions based on specific requirements. This segmentation is vital for companies such as Pozyx Labs, Advanced Realtime Tracking (ART), and Inmotio to effectively reach their target markets and provide customized solutions. The competitive landscape features a mix of established players and innovative startups, driving innovation and competition in the market. The geographical distribution of the IPTS market reveals significant potential across various regions. North America, with its advanced technological infrastructure and high adoption rates, currently holds a significant market share. However, the Asia-Pacific region, driven by rapid urbanization and growing investments in smart city infrastructure, is expected to exhibit substantial growth in the coming years. Europe also presents a significant market opportunity due to the rising focus on improving operational efficiency and security in various public and private sectors. The continued expansion into emerging markets in the Middle East & Africa and South America will further contribute to the overall growth of the global IPTS market. The forecast period suggests continued strong growth, driven by the factors outlined above, pointing to a promising future for this technology.

The indoor positioning technology market is experiencing robust growth, driven by increasing demand across commercial, municipal, and personal applications. The market's expansion is fueled by several key factors: the proliferation of smartphones and smart devices equipped with location-aware capabilities; the rising adoption of IoT (Internet of Things) devices requiring precise indoor location data; and the increasing need for efficient asset tracking and management in various sectors like healthcare, retail, and logistics. Technological advancements, particularly in Ultra-Wideband (UWB) and Bluetooth technologies, are enhancing accuracy and reliability, further stimulating market growth. While the initial investment in infrastructure can be a restraint, the long-term benefits in terms of operational efficiency and enhanced customer experience are driving adoption. We estimate the market size in 2025 to be approximately $2.5 billion, with a Compound Annual Growth Rate (CAGR) of 15% projected through 2033, reaching approximately $8 billion by the end of the forecast period. Significant regional variations exist, with North America and Europe currently holding the largest market shares due to early adoption and mature technological infrastructure. However, the Asia-Pacific region is expected to witness the fastest growth rate due to rapid urbanization and increasing investments in smart city initiatives. The diverse range of technologies employed, including infrared, ultrasonic, RFID, Bluetooth, Wi-Fi, ZigBee, and UWB, provides various solutions tailored to specific application needs and budget constraints. The segmentation of the market by application and technology highlights the diverse opportunities within this sector. The commercial segment, encompassing retail, warehousing, and offices, dominates the market due to the strong demand for improved operational efficiency and enhanced customer experiences. Municipal applications, including smart city initiatives and public safety, are experiencing significant growth, driven by the need for improved resource allocation and emergency response. The personal segment, primarily driven by location-based services and gaming, is showing gradual growth. Among technologies, UWB is gaining traction due to its high accuracy and reliability, though the other technologies maintain substantial market share depending on the specific application and cost considerations. Competitive landscape is characterized by a mix of established players with extensive portfolios and innovative startups focusing on niche applications and advanced technologies. Continued innovation in both hardware and software, along with expanding partnerships between technology providers and system integrators, will shape the future of the indoor positioning technology market.

The jurisdictional numerical data of the Taiwan area land administration office adopts the TWD97_119 coordinate system.

This dataset was created as suplementary material for research article: Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms This package contains packet capture files of 802.11 probe requests captured at Geotec office at University Jaume I, Spain by 5 ESP32 microcontrollers. The packet capture files are in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package). The data are split between radio map data captured at all accessible reference positions in our office spread in 1m grid and evaluation data gathered alligned to 0.5m grid, as well as in hard to access locations. The location the data were collected are available in the office. The dataset has 4 parts, and all subsets of the dataset can be generated from the captured pcap files: Data This folder contains pcap files from all 5 ESP32 stations representing the whole radio environment map. The folder name stands for each of the 5 ESP32 sniffer stations and the name of the file points to a reference location the data were captured in. Example of the coordinates matching the reference location grid names are in following table: Data Point Coordinates X Y X Y ... A1 0.85 0.1 B1 1.85 0.1 ... A2 0.85 1.1 B2 1.85 1.1 ... A3 0.85 2.1 B3 1.85 2.1 ... ... ... ... ... ... ... ... A11 0.85 10.1 B11 1.85 10.1 ... Data_Eval This folder contains pcap files from all 5 ESP32 stations with data captured at 31 locations not found in the original reference location grid. The naming corresponds to the X and Y location in which the data were collected. Processed_Data Additionally, there are 3 folders with processed CSV files. One folder that combines all radio map values, second folder contains combined evaluation values and third is with linearly interpolated radio map values. The CSV files are in a format: X, Y, RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5 Data_Scenarios This folder for the ease of use, contains data for exact reproducibility of our results in the paper. There 14 scenarios described in the following table: Scenario Descriptions Data Name Scenario Description GPR00 Only measured data, 50 samples per reference position GPR01 Measured data with empty spots filled using Linear interpolation, 50 samples per reference position GPR02 Gaussian Regression trained only on measured data - 1m output grid, 50 samples per reference position GPR03 Gaussian Regression trained only on measured data - 0.5m output grid, 50 samples per reference position GPR04 Gaussian Regression trained on linearly interpolated data - 1m output grid, 50 samples per reference position GPR05 Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 50 samples per reference position GPR06 Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 50 samples per reference position GPR07 Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 50 samples per reference position GPR08 Gaussian Regression trained only on measured data - 1m output grid, 1 sample per reference position GPR09 Gaussian Regression trained only on measured data - 0.5m output grid, 1 sample per reference position GPR10 Gaussian Regression trained on linearly interpolated data - 1m output grid, 1 sample per reference position GPR11 Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 1 sample per reference position GPR12 Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 1 sample per reference position GPR13 Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 1 sample per reference position The folder contains 4 files for each scenario. The Beginning of the filename corresponds to the data name, with suffix describing what data are in the file. The descriptions of used suffixes are in the following table: File Suffix Descriptions Suffix Suffix Description _trncrd Training Labels _trnrss Training RSSI Values _tstcrd Evaluation Labels _tstrss Evaluation RSSI Values These data are in format compatible with systems that apart from X and Y coordinates also detect, building, floor etc. The RSSI data are in format: RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5 The Labels are in format: (Since we only use positioning in 1 office, apart X and Y coordinates are set to 0) X, Y, 0, 0, 0

Position Annotated BLE RSSI Dataset for Indoor Localization

Please contact us or comment below for any inconvenience, questions or suggestions.

UPDATE by 29 May 2022

The video files are removed due to space constraints. Please contact us in order to access the videos.

What?

This dataset is intended for the researchers that work in indoor positioning domain and want to use positioning parameters that are labeled with highly precise ground truth positions. We provide signal parameters collected while navigating an emitter at different paces in an indoor environment, and their position labels collected using a more precise method (with a median precision of ~0.04 m) and registered to the signal parameters rigorously.

We collect the data using a specially designed setup of cameras and a Bluetooth beacon. A network of distributed Bluetooth sensors collect the received signal strength indicators (RSSI) of the captured packages emitted by a navigated Bluetooth beacon. Simultaneously, a two camera system captures videos from the environment that is decorated with a dense set of augmented reality (AR) tags. The AR-based visual system provides highly precise position information. These precise positions can be used as the ground truth for positioning. Any wireless-based inference about the positions can now be evaluated accurately using these precise position labels.

How?

We collect two different data types simultaneously while navigating the beacon: (i) RSSI data captured by the sensors from the BLE signals emitted by a mobile beacon, and (ii) videos of the environment by the two cameras looking forwards and backwards.

(i) There are 12 sensors (BLE dongles) stationed in the environment. Each three of these sensors are driven by one Raspberry Pi 3b+, thus 4 computers are used for RSSI data collection. The computers are connected through ethernet in order to mitigate the wireless signal interferences due to WiFi. The orchestration of data collection procedure was achieved by employing ROS Melodic as middleware. In our setup, each sensor is associated with a ROS node, and each captured RSSI data is published as a ROS message by its corresponding ROS topic. As topics can be subscribed by any node in the ROS network, we collect the data in another monitoring machine easily and simultaneously.

(ii) We attached two GoPro Hero 4 motion cameras together facing opposite directions. The area was decorated with 30 ArUco markers large enough to be detected from anywhere in the area. While navigating the beacon and collecting the data through the ROS network, we also took videos of the navigation with both of the cameras. The videos are processed using a custom software. The software can synchronize the frames, detects multiple markers in the frames, and estimates their poses. Combining these dynamic poses with the previously measured pose information with respect to the world coordinate frame, we obtain several camera pose candidates in each frame. These candidates are filtered by different techniques to yield a single pose of the cameras, thus the dynamic position of the beacon.

RSSI data from (i) and the poses from (ii) are then synchronized in time to obtain "Position annotated BLE RSSI data" for each journey in the area. Please see the associated article [Daniş et al, PMC, 2022] for details.

Why?

We have been working in the indoor positioning with wireless signal parameters since 2016. At this period, we have applied different techniques of state space models from particle filters to forward algorithm and different setups to collect data from mobile sensors by stationary beacons or mobile beacons by stationary beacons. Wireless signal parameters like RSSI are hard to work with on their own, because of their weaknesses due to environmental factors. Therefore, we believe that this is still an open ground for various research topics.

However this dataset focuses on another challenge about wireless signal parameters which has not been considered sufficiently important: Can we accurately evaluate the microlocations estimated by the positioning algorithms? Moreover, taking one step further, can we evaluate the trajectories estimated by the tracking algorithms?

Where?

The dataset was collected in the office space of the Telecommunications and Informatics Technologies Research Center at Boğaziçi University.

Thanks

This data collection challenge would not be possible without the help of Dr. A. Teoman Naskali.

Share

We are sharing code for parsing some of the file types in the dataset. You can find the associated notebook under the name "parsers".

Research

• For a complete introduction to the distributed sensing setup and the method that collects and synchronizes the data, please refer to [Daniş et al, PMC, 2022].
• In order to see how to build a Sequential Monte Carlo (Particle Filter) based algorithm that employs these data to infer the positions of a navigated emitter, please see [Daniş et al, Access, 2021].
• To estimate probabilistic radio frequency maps using Affine Wasserstein Combination that takes stationary information, please refer to [Daniş et al, Sensors, 2017].
• To infer probabilistic radio frequency maps using neural networks, please refer to [Güler et al, IPIN, 2019].

References

[Daniş et al., PMC, 2022] F. Serhan Daniş and A. Teoman Naskali and A. Taylan Cemgil and Cem Ersoy. An Indoor Localization Dataset and Data Collection Framework with High Precision Position Annotation, Pervasive and Mobile Computing, 81, 101554, Apr. 2022.

[Daniş et al., Access, 2021] F. S. Daniş, A. T. Cemgil, and C. Ersoy. Adaptive sequential monte carlo filter for indoor positioning and tracking with bluetooth low energy beacons. IEEE Access, 9:37022–37038, 2021.

[Daniş and Cemgil, Sensors, 2017] FS Daniş and AT Cemgil, “Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons,” Sensors, vol. 17, no. 11, p. 2484, Oct. 2017.

[Güler et al., IPIN, 2019] Sila Guler, F. Serhan Danis, and Ali Taylan Cemgil. Radio map estimation with neural networks and active learning for indoor localization. In Francesco Potorti, Valérie Renaudin, Kyle O’Keefe, and Filippo Palumbo, editors, Proceedings of the Tenth International Conference on Indoor Positioning and Indoor Navigation - Work-in-Progress Papers, number 2498 in CEUR Workshop Proceedings, pages 25–31, Aachen, 2019.

The global indoor positioning and tracking system (IPS) market is anticipated to observe significant growth in the coming years, driven by the increasing demand for location-based services in various sectors. The market is witnessing a growing adoption in applications such as malls, airports, offices, stadiums, and schools, as these systems provide real-time visibility and tracking of assets, personnel, and customers. The market size is estimated to be valued at USD 2993 million in 2025 and is projected to reach USD 5543 million by 2033, expanding at a CAGR of 9.1% during the forecast period (2025-2033). The market is witnessing increased investments in research and development (R&D) activities by key players to enhance the accuracy and efficiency of indoor positioning systems. The growing adoption of smartphones and the proliferation of Internet of Things (IoT) devices are also driving the market growth, as these technologies facilitate the seamless integration of indoor positioning and tracking solutions. Additionally, the increasing demand for indoor navigation and wayfinding services is fueling market growth, as these systems provide convenient and user-friendly navigation experiences in indoor environments.

Dataset of paper "Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation".

The measurement data contains indoor mapping results using millimeter-wave 5G NR signals at 28 GHz. The measurement campaign was conducted in an indoor office environment in Hervanta Campus of Tampere University. Six different sets of measurements contain the range profiles after the proposed radar processing. The shared data contains the IQ data of both transmit and receive signals used during the measurement campaign.

The file "main.m" shows how to process and plot the shared data.