scholarly journals Robust Positioning Performance in Indoor Environments

2019 ◽  
Vol 94 ◽  
pp. 02001
Author(s):  
Allison Kealy ◽  
Guenther Retscher ◽  
Yan Li ◽  
Thomas Gonzales ◽  
Salil Goel ◽  
...  
Keyword(s):  
Indoor Positioning ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Cooperative Positioning ◽  
Emergency Responders ◽  
Performance Improvements ◽  
Asset Tracking ◽  
Experimental Campaign ◽  
Cost Efficient ◽  
Initial Results

Increasingly, safety and liability critical applications require GNSS-like positioning metrics in environments where GNSS cannot work. Indoor navigation for the vision impaired and other mobility restricted individuals, emergency responders and asset tracking in buildings demand levels of positioning accuracy and integrity that cannot be satisfied by current indoor positioning technologies and techniques. This paper presents the challenges facing positioning technologies for indoor positioning and presents innovative algorithms and approaches that aim to enhance performance in these difficult environments. The overall aim is to achieve GNSS-like performance in terms of autonomous, global, infrastructure free, portable and cost efficient. Preliminary results from a real-world experimental campaign conducted as part of the joint FIG Working Group 5.5 and IAG Sub-commission 4.1 on multi-sensor systems, demonstrate performance improvements based on differential Wi-Fi (DWi-Fi) and cooperative positioning techniques. The techniques, experimental schema and initial results will be fully documented in this paper.

scholarly journals An Evidential Framework for Localization of Sensors in Indoor Environments

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 318 ◽  
Author(s):  
Daniel Alshamaa ◽  
Farah Mourad-Chehade ◽  
Paul Honeine ◽  
Aly Chkeir
Keyword(s):  
Indoor Localization ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
People Tracking ◽  
Multiple Sensors ◽  
Dempster Shafer Theory ◽  
Asset Tracking ◽  
Evidence Framework ◽  
Autonomous Robot Navigation ◽  
Shafer Theory

Indoor localization has several applications ranging from people tracking and indoor navigation, to autonomous robot navigation and asset tracking. We tackle the problem as a zoning localization where the objective is to determine the zone where the mobile sensor resides at any instant. The decision-making process in localization systems relies on data coming from multiple sensors. The data retrieved from these sensors require robust fusion approaches to be processed. One of these approaches is the belief functions theory (BFT), also called the Dempster–Shafer theory. This theory deals with uncertainty and imprecision with a theoretically attractive evidential reasoning framework. This paper investigates the usage of the BFT to define an evidence framework for estimating the most probable sensor’s zone. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods.

scholarly journals Indoor Navigation System

2020 ◽  
pp. 140-145
Author(s):  
Alisha Tandulwar ◽  
Nikita Tembhurne ◽  
Shubham Kokatey ◽  
Ranjana Shende
Keyword(s):  
Indoor Positioning ◽  
Indoor Navigation ◽  
Outdoor Environment ◽  
Indoor Environments ◽  
Interesting Approach ◽  
Working Solution ◽  
Indoor Navigation System ◽  
Set Up ◽  
Similar Solutions ◽  
System 1

Today it is possible to position wireless devices such as smartphones in an outdoor environment, using GPS, global positioning system.[1] Unfortunately this technology does not work in indoor environments. Being able to navigate smartphones indoors could be beneficial in many different environments. In hospitals when tracking hospital beds and patients, in prisons in order to track prisoners, in malls when a customer is searching for a store etc.[1] Indoor navigation could also be used to analyze human behavior. There are many different methods and techniques existing today that could benefit to an indoor navigation solution, all of them providing different results. Some are friendlier towards larger scaled areas providing less satisfying precision, meanwhile others provide higher precision but increases significantly in cost for larger scaled areas. In this project a significant amount of time was spent on researching different techniques for indoor positioning as well as analyzing the current state of the art and the[2] market of existing indoor positioning solutions. It provided a fully working solution to position on device. It created by the structure of the building and different objects in the indoor environment, together with a set of maps use to navigate. The work has resulted in a fully working server together with an android application.[3] The solution is an interesting approach to indoor navigation. It is fast and easy to set up and performs reasonably well compared to similar solutions.[4]

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.

Robustness in Fingerprinting-Based Indoor Positioning Systems

2018 ◽  
pp. 88-141 ◽  
Author(s):  
Shih-Hau Fang
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Signal Strength ◽  
Histogram Equalization ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Location Based Services ◽  
Indoor Environments ◽  
Positioning Systems

Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. Received signal strength (RSS), mostly utilized in Wi-Fi fingerprinting systems, is known to be unreliable due to two reasons: orientation mismatch and variations in hardware. This chapter introduces an approach based on histogram equalization to compensate for orientation mismatch in robust Wi-Fi localization. The proposed method involves converting the temporal-spatial radio signal strength into a reference function (i.e., equalizing the histogram). This chapter also introduces an enhanced positioning feature, which is called delta-fused principal strength, to enhance the robustness of Wi-Fi localization against the problem of heterogeneous hardware. This algorithm computes the pairwise delta RSS and then integrates with RSS using principal component analysis. The proposed methods effectively and efficiently improve the robustness of location estimation in the presence of mismatch orientation and hardware variations, respectively.

scholarly journals Deep Learning-Based Positioning of Visually Impaired People in Indoor Environments

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6238
Author(s):  
Payal Mahida ◽  
Seyed Shahrestani ◽  
Hon Cheung
Keyword(s):  
Navigation System ◽  
Visually Impaired ◽  
Indoor Environment ◽  
Inertial Sensor ◽  
User Interaction ◽  
Indoor Navigation ◽  
Sensor Data ◽  
Indoor Environments ◽  
Visually Impaired People ◽  
Indoor Navigation System

Wayfinding and navigation can present substantial challenges to visually impaired (VI) people. Some of the significant aspects of these challenges arise from the difficulty of knowing the location of a moving person with enough accuracy. Positioning and localization in indoor environments require unique solutions. Furthermore, positioning is one of the critical aspects of any navigation system that can assist a VI person with their independent movement. The other essential features of a typical indoor navigation system include pathfinding, obstacle avoidance, and capabilities for user interaction. This work focuses on the positioning of a VI person with enough precision for their use in indoor navigation. We aim to achieve this by utilizing only the capabilities of a typical smartphone. More specifically, our proposed approach is based on the use of the accelerometer, gyroscope, and magnetometer of a smartphone. We consider the indoor environment to be divided into microcells, with the vertex of each microcell being assigned two-dimensional local coordinates. A regression-based analysis is used to train a multilayer perceptron neural network to map the inertial sensor measurements to the coordinates of the vertex of the microcell corresponding to the position of the smartphone. In order to test our proposed solution, we used IPIN2016, a publicly-available multivariate dataset that divides the indoor environment into cells tagged with the inertial sensor data of a smartphone, in order to generate the training and validating sets. Our experiments show that our proposed approach can achieve a remarkable prediction accuracy of more than 94%, with a 0.65 m positioning error.

RFID-Enabled Location Determination Within Indoor Environments

2009 ◽  
Vol 1 (4) ◽  
pp. 63-86 ◽  
Author(s):  
Kevin Curran ◽  
Stephen Norrby
Keyword(s):  
Supply Chain ◽  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Automatic Identification ◽  
Indoor Environments ◽  
Location Determination ◽  
Asset Tracking ◽  
Current Location ◽  
Toll Collection ◽  
Main Components

The ability to track the real-time location and movement of items or people offers a broad range of useful applications in areas such as safety, security and the supply chain. Current location determination technologies, however, have limitations that heavily restrict how and where these applications are implemented, including the cost, accuracy of the location calculation and the inherent properties of the system. The Global Positioning System (GPS), for example, cannot function indoors and is useful only over large-scaled areas such as an entire city. Radio Frequency Identification (RFID) is an automatic identification technology which has seen increasingly prominent use over the last few decades. The technology uses modulated Radio Frequency signals to transfer data between its two main components, the reader and the transponder. Its many applications include supply chain management, asset tracking, security clearance and automatic toll collection. In recent years, advancements in the technology have allowed the location of transponders to be calculated while interfacing with the reader. This article documents an investigation into using an active RFID based solution for tracking.

scholarly journals Leveraging Visual Place Recognition to Improve Indoor Positioning with Limited Availability of WiFi Scans

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3657 ◽  
Author(s):  
Michał R. Nowicki ◽  
Piotr Skrzypczyński
Keyword(s):  
Indoor Localization ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Positioning System ◽  
Indoor Environments ◽  
University Campus ◽  
Sources Of Information ◽  
Visual Place Recognition ◽  
Indoor Positioning System

WiFi-based fingerprinting is promising for practical indoor localization with smartphones because this technique provides absolute estimates of the current position, while the WiFi infrastructure is ubiquitous in the majority of indoor environments. However, the application of WiFi fingerprinting for positioning requires pre-surveyed signal maps and is getting more restricted in the recent generation of smartphones due to changes in security policies. Therefore, we sought new sources of information that can be fused into the existing indoor positioning framework, helping users to pinpoint their position, even with a relatively low-quality, sparse WiFi signal map. In this paper, we demonstrate that such information can be derived from the recognition of camera images. We present a way of transforming qualitative information of image similarity into quantitative constraints that are then fused into the graph-based optimization framework for positioning together with typical pedestrian dead reckoning (PDR) and WiFi fingerprinting constraints. Performance of the improved indoor positioning system is evaluated on different user trajectories logged inside an office building at our University campus. The results demonstrate that introducing additional sensing modality into the positioning system makes it possible to increase accuracy and simultaneously reduce the dependence on the quality of the pre-surveyed WiFi map and the WiFi measurements at run-time.

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