Constructing Floor Plan through Smoke Using Ultra Wideband Radar

2021 ◽  
Vol 5 (4) ◽  
pp. 1-29
Author(s):  
Weiyan Chen ◽  
Fusang Zhang ◽  
Tao Gu ◽  
Kexing Zhou ◽  
Zixuan Huo ◽  
...  
Keyword(s):  
Spatial Information ◽  
Low Cost ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Ultra Wideband ◽  
Indoor Environments ◽  
Floor Plan ◽  
Construction Methods ◽  
Median Error ◽  
Indoor Scenarios

Floor plan construction has been one of the key techniques in many important applications such as indoor navigation, location-based services, and emergency rescue. Existing floor plan construction methods require expensive dedicated hardware (e.g., Lidar or depth camera), and may not work in low-visibility environments (e.g., smoke, fog or dust). In this paper, we develop a low-cost Ultra Wideband (UWB)-based system (named UWBMap) that is mounted on a mobile robot platform to construct floor plan through smoke. UWBMap leverages on low-cost and off-the-shelf UWB radar, and it is able to construct an indoor map with an accuracy comparable to Lidar (i.e., the state-of-the-art). The underpinning technique is to take advantage of the mobility of radar to form virtual antennas and gather spatial information of a target. UWBMap also eliminates both robot motion noise and environmental noise to enhance weak reflection from small objects for the robust construction process. In addition, we overcome the limited view of single radar by combining multi-view from multiple radars. Extensive experiments in different indoor environments show that UWBMap achieves a map construction with a median error of 11 cm and a 90-percentile error of 26 cm, and it operates effectively in indoor scenarios with glass wall and dense smoke.

Trilateration approaches for seamless out-/indoor GNSS and Wi-Fi smartphone positioning

2019 ◽  
Vol 13 (1) ◽  
pp. 47-61
Author(s):  
Guenther Retscher ◽  
Jonathan Kleine ◽  
Lisa Whitemore
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Ground Truth ◽  
Mobile User ◽  
Location Based Services ◽  
Raspberry Pi ◽  
Indoor Environments ◽  
Point Location ◽  
Point Determination ◽  
The Relationship

Abstract More and more sensors and receivers are found nowadays in smartphones which can enable and improve positioning for Location-based Services and other navigation applications. Apart from inertial sensors, such as accelerometers, gyroscope and magnetometer, receivers for Wireless Fidelity (Wi-Fi) and GNSS signals can be employed for positioning of a mobile user. In this study, three trilateration methods for Wi-Fi positioning are investigated whereby the influence of the derivation of the relationship between the received signal strength (RSS) and the range to an Access Points (AP) are analyzed. The first approach is a straightforward resection for point determination and the second is based on the calculation of the center of gravity in a triangle of APs while weighting the received RSS. In the third method a differential approach is employed where as in Differential GNSS (DGNSS) corrections are derived and applied to the raw RSS measurements. In this Differential Wi-Fi (DWi-Fi) method, reference stations realized by low-cost Raspberry Pi units are used to model temporal RSS variations. In the experiments in this study two different indoor environments are used, one in a laboratory and the second in the entrance of an office building. The results of the second and third approach show position deviations from the ground truth of around 2 m in dependence of the geometrical point location. Furthermore, the transition between GNSS positioning outdoors and Wi-Fi localization indoors in the entrance area of the building is studied.

An Intelligent Navigation Solution for Land Mobile Location-Based Services

2002 ◽  
Vol 55 (2) ◽  
pp. 225-240 ◽  
Author(s):  
Stephen Scott-Young ◽  
Allison Kealy
Keyword(s):  
Firm Growth ◽  
Spatial Information ◽  
Inertial Sensors ◽  
Low Cost ◽  
Dead Reckoning ◽  
Location Based Services ◽  
Geographical Information ◽  
Navigation Systems ◽  
Navigation Solution ◽  
Lower Accuracy

The increasing availability of small, low-cost GPS receivers has established a firm growth in the production of Location-Based Services (LBS). LBS, such as in-car navigation systems, are not necessarily reliant on high accuracy but a continuous positioning service. When available, the accuracy provided by the standard positioning service (SPS) of 30 metres, 95% of the time is often acceptable. The reality is, however, that GPS does not work in all situations, and it is therefore common to integrate GPS with additional sensors. The use of low-cost inertial sensors alone during GPS signal outage is severely restricted due to the accumulation of errors that is inherent with such dead reckoning (DR) systems. Through the integration of spatial information with real-time positioning sensors, intelligence can be added to the land mobile navigation solution. The information contained within a Geographical Information System (GIS) provides additional observations that can be used to improve the navigation result. With this approach, the solution is not dependent on the performance capabilities of the navigation sensors alone. This enables the use of lower accuracy navigation devices, allowing low-cost systems to provide a sustained, viable navigation solution despite long-term GPS outages. Practical results are presented comparing solutions obtained from a hand-held GPS receiver to a gyroscope and odometer.

scholarly journals Multi-Sensor Accurate Forklift Location and Tracking Simulation in Industrial Indoor Environments

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1152 ◽  
Author(s):  
Valentín Barral ◽  
Pedro Suárez-Casal ◽  
Carlos J. Escudero ◽  
José A. García-Naya
Keyword(s):  
Inertial Sensor ◽  
Wireless Channel ◽  
Good Choice ◽  
Ultra Wideband ◽  
Positioning System ◽  
Indoor Environments ◽  
Location Algorithm ◽  
Industrial Environments ◽  
Indoor Scenarios ◽  
Industrial Problem

Location and tracking needs are becoming more prominent in industrial environments nowadays. Process optimization, traceability or safety are some of the topics where a positioning system can operate to improve and increase the productivity of a factory or warehouse. Among the different options, solutions based on ultra-wideband (UWB) have emerged during recent years as a good choice to obtain highly accurate estimations in indoor scenarios. However, the typical harsh wireless channel conditions found inside industrial environments, together with interferences caused by workers and machinery, constitute a challenge for this kind of system. This paper describes a real industrial problem (location and tracking of forklift trucks) that requires precise internal positioning and presents a study on the feasibility of meeting this challenge using UWB technology. To this end, a simulator of this technology was created based on UWB measurements from a set of real sensors. This simulator was used together with a location algorithm and a physical model of the forklift to obtain estimations of position in different scenarios with different obstacles. Together with the simulated UWB sensor, an additional inertial sensor and optical sensor were modeled in order to test its effect on supporting the location based on UWB. All the software created for this work is published under an open-source license and is publicly available.

UWB and MEMS Based Indoor Navigation

2008 ◽  
Vol 61 (3) ◽  
pp. 369-384 ◽  
Author(s):  
Sylvain Pittet ◽  
Valérie Renaudin ◽  
Bertrand Merminod ◽  
Michel Kasser
Keyword(s):  
Indoor Navigation ◽  
Ultra Wideband ◽  
Indoor Environments ◽  
Selection Methods ◽  
Pedestrian Navigation ◽  
Radio Signals ◽  
Uwb Radio ◽  
Mechanical Sensors ◽  
Inertial Measurements ◽  
Reference Trajectories

Thanks to its physical characteristics, Ultra-wideband (UWB) is one of the most promising technologies for indoor pedestrian navigation. UWB radio localisation systems however experience multipath phenomena that decrease the precision and the reliability of the user's location. To cope with complex indoor environments, UWB radio signals are coupled with inertial measurements from Micro Electro Mechanical Sensors (MEMS) in an extended Kalman filter. Improved performances of the filter are presented and compared with reference trajectories and with pure inertial solutions. Only specific selection methods enable this improvement by detecting and removing outliers in the raw localisation data.

scholarly journals NLOS Identification and Mitigation Using Low-Cost UWB Devices

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3464 ◽  
Author(s):  
Valentín Barral ◽  
Carlos J. Escudero ◽  
José A. García-Naya ◽  
Roberto Maneiro-Catoira
Keyword(s):  
Low Cost ◽  
Estimation Error ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Distance Measurements ◽  
Indoor Location ◽  
Indoor Scenarios ◽  
Indoor Location Systems ◽  
Clear Line ◽  
Non Line Of Sight

Indoor location systems based on ultra-wideband (UWB) technology have become very popular in recent years following the introduction of a number of low-cost devices on the market capable of providing accurate distance measurements. Although promising, UWB devices also suffer from the classic problems found when working in indoor scenarios, especially when there is no a clear line-of-sight (LOS) between the emitter and the receiver, causing the estimation error to increase up to several meters. In this work, machine learning (ML) techniques are employed to analyze several sets of real UWB measurements, captured in different scenarios, to try to identify the measurements facing non-line-of-sight (NLOS) propagation condition. Additionally, an ulterior process is carried out to mitigate the deviation of these measurements from the actual distance value between the devices. The results show that ML techniques are suitable to identify NLOS propagation conditions and also to mitigate the error of the estimates when there is LOS between the emitter and the receiver.

scholarly journals Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1995
Author(s):  
Danshi Sun ◽  
Erhu Wei ◽  
Zhuoxi Ma ◽  
Chenxi Wu ◽  
Shiyi Xu
Keyword(s):  
Spatial Information ◽  
Indoor Localization ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Communication Services ◽  
Received Signal Strength Indicator ◽  
Location Data ◽  
Complex Building ◽  
Enhanced Particle Swarm Optimization ◽  
Dynamic Inertia

Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user’s indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers’ length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user’s location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user’s locations in a complex building without complex calibration as compared to other recent methods.

scholarly journals Cost-Effective Proximity Detection and Filtering of Unreliable Results Based on BLE RSSI and IMU Readings Using Smartphones for Indoor Environments

2021 ◽  
Author(s):  
Katarzyna Filus ◽  
Sławomir Nowak ◽  
Joanna Domańska ◽  
Jakub Duda
Keyword(s):  
Low Cost ◽  
Real Life ◽  
Great Majority ◽  
Cost Effective ◽  
Location Based Services ◽  
Measurement Unit ◽  
Learning Activity ◽  
External Information ◽  
Indoor Environments ◽  
Boosted Decision Trees

Abstract Indoor environments are a major challenge in the domain of location-based services due to the inability to use GPS. Currently, Bluetooth Low Energy has been the most commonly used technology for such services due to its low cost, low power consumption, ubiquitous availability in smartphones and the dependence of the signal strength on the distance between devices. The article proposes a system that detects the proximity of a moving object with respect to static points (anchors), evaluates the quality of this prediction and filters out the unreliable results based on custom metrics. We define three metrics: two matrics based on RSSI and Intertial Measurement Unit (IMU) readings and one joint metric. This way the filtering is based on both, the external information (RSSI) and the internal information (IMU). To process the IMU data, we use machine learning activity recognition models (we apply feature selection and compare three models and choose the best one-Gradient Boosted Decision Trees). The proposed system is flexible and can be easily customized. The great majority of operations can be conducted directly on smartphones. The solution is easy to implement, cost-efficient and can be deployed in real-life applications (MICE industry, museums, industry).

scholarly journals EXTRACTING TOPOLOGICAL RELATIONS BETWEEN INDOOR SPACES FROM POINT CLOUDS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 401-406 ◽  
Author(s):  
H. Tran ◽  
K. Khoshelham ◽  
A. Kealy ◽  
L. Díaz-Vilariño
Keyword(s):  
Emergency Management ◽  
Principal Components ◽  
Point Clouds ◽  
3D Models ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Indoor Environments ◽  
Topological Relations ◽  
Indoor Location ◽  
Indoor Spaces

3D models of indoor environments are essential for many application domains such as navigation guidance, emergency management and a range of indoor location-based services. The principal components defined in different BIM standards contain not only building elements, such as floors, walls and doors, but also navigable spaces and their topological relations, which are essential for path planning and navigation. We present an approach to automatically reconstruct topological relations between navigable spaces from point clouds. Three types of topological relations, namely containment, adjacency and connectivity of the spaces are modelled. The results of initial experiments demonstrate the potential of the method in supporting indoor navigation.

scholarly journals Automatic Extraction of Indoor Spatial Information from Floor Plan Image: A Patch-Based Deep Learning Methodology Application on Large-Scale Complex Buildings

2021 ◽  
Vol 10 (12) ◽  
pp. 828
Author(s):  
Hyunjung Kim ◽  
Seongyong Kim ◽  
Kiyun Yu
Keyword(s):  
3D Modeling ◽  
Detection Rate ◽  
Large Scale ◽  
Spatial Information ◽  
Recognition Accuracy ◽  
Indoor Navigation ◽  
Data Driven ◽  
Automatic Extraction ◽  
Floor Plan ◽  
Plan Analysis

Automatic floor plan analysis has gained increased attention in recent research. However, numerous studies related to this area are mainly experiments conducted with a simplified floor plan dataset with low resolution and a small housing scale due to the suitability for a data-driven model. For practical use, it is necessary to focus more on large-scale complex buildings to utilize indoor structures, such as reconstructing multi-use buildings for indoor navigation. This study aimed to build a framework using CNN (Convolution Neural Networks) for analyzing a floor plan with various scales of complex buildings. By dividing a floor plan into a set of normalized patches, the framework enables the proposed CNN model to process varied scale or high-resolution inputs, which is a barrier for existing methods. The model detected building objects per patch and assembled them into one result by multiplying the corresponding translation matrix. Finally, the detected building objects were vectorized, considering their compatibility in 3D modeling. As a result, our framework exhibited similar performance in detection rate (87.77%) and recognition accuracy (85.53%) to that of existing studies, despite the complexity of the data used. Through our study, the practical aspects of automatic floor plan analysis can be expanded.

scholarly journals TOWARDS INDOORGML 2.0: UPDATES AND CASE STUDY ILLUSTRATIONS

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 337-344
Author(s):  
A. A. Diakité ◽  
S. Zlatanova ◽  
A. F. M. Alattas ◽  
K. J. Li
Keyword(s):  
Asset Management ◽  
Spatial Information ◽  
Location Based Services ◽  
Indoor Environments ◽  
Modelling Language ◽  
Indoor Location ◽  
Model Driven ◽  
Model Driven Approach ◽  
Indoor Spaces

Abstract. Indoor environments differ from outdoor in many aspects. This, added to the limitations faced by other common standards for urban features reinforced the need of setting a dedicated standard for indoor applications. IndoorGML was born in this context to provide the basic concepts, data models, and standard that meet the requirements of indoor spatial applications. Indoor spatial information can be generally classified into two categories: indoor objects such as architectural components (walls, stairs, slabs) and interior facilities (furniture); indoor spaces such as cavities (rooms and corridors) or virtual subdivision (sensor and legal spaces). Handling both information is necessary to support applications ranging from Indoor location-based services (LBS), indoor route analysis or indoor geo-tagging to building and asset management. In this paper, we present the proposed changes to the second version of IndoorGML, under preparation and intended to provide the necessary support for applications using information from those two categories. IndoorGML 2.0 is open to all applications that rely on indoor spaces and require analysis that can be performed on a network, extracted from those spaces utilizing neighbourhood relationships. It follows a model-driven approach, i.e. all concepts are presented by the Unified Modelling Language, from which technical implementations are derived (GML, JSON, SQL, etc.). We present the proposed changes to the previous version, illustrate a way of representing indoor objects other than spaces and discuss several use cases of the standard.

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