Relevant bibliographies by topics / Indoor positional navigation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Indoor positional navigation'

Author: Grafiati
Published: 29 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Indoor positional navigation"

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Subhash Reddy, S., and Y. Bhaskar Rao. "Indoor Navigation System for Blind People Using VLC." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 77. http://dx.doi.org/10.14419/ijet.v7i3.27.17659.

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We propose an indoor navigation system that utilizes visible light communication technology, which employs LED lights and a geomagnetic correction method, aimed at supporting visually impaired people who travel indoors. To verify the effectiveness of this system, we conducted an experiment targeting visually impaired people. Although acquiring accurate positional information and detecting directions indoors is difficult, we confirmed that using this system, accurate positional information and travel direction can be obtained utilizing visible light communication technology, which employs LED lights, and correcting the values of the geomagnetic sensor integrated in a Smartphone.
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Irshad, Liu, Arshad, Sohail, Murthy, Khokhar, and Uba. "A Novel Localization Technique Using Luminous Flux." Applied Sciences 9, no. 23 (November 21, 2019): 5027. http://dx.doi.org/10.3390/app9235027.

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As global navigation satellite system (GNNS) signals are unable to enter indoor spaces, substitute methods such as indoor localization-based visible light communication (VLC) are gaining the attention of researchers. In this paper, the systematic investigation of a VLC channel is performed for both direct and indirect line of sight (LoS) by utilizing the impulse response of indoor optical wireless channels. In order to examine the localization scenario, two light-emitting diode (LED) grid patterns are used. The received signal strength (RSS) is observed based on the positional dilution of precision (PDoP), a subset of the dilution of precision (DoP) used in global navigation satellite system (GNSS) positioning. In total, 31 × 31 possible positional tags are set for a given PDoP configuration. The values for positional error in terms of root mean square error (RMSE) and the sum of squared errors (SSE) are taken into consideration. The performance of the proposed approach is validated by simulation results according to the selected indoor space. The results show that the position accuracy enhanced is at short range by 24% by utilizing the PDoP metric. As confirmation, the modeled accuracy is compared with perceived accuracy results. This study determines the application and design of future optical wireless systems specifically for indoor localization.
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Vieira, M. A., M. Vieira, V. Silva, P. Louro, L. Mateus, and P. Vieira. "Indoor positioning using a-SiC:H technology." MRS Advances 1, no. 55 (2016): 3685–90. http://dx.doi.org/10.1557/adv.2016.381.

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ABSTRACTThe nonlinear property of SiC multilayer devices under Ultra Violet (UV) irradiation is used to design an optical processor for indoor positioning. The transducers combine the simultaneous demultiplexing operation with the photodetection and self-amplification. Moreover, we present a way to achieve indoor positioning using the parity bits and the navigation syndrome. A 4 bit representation with the original string colour message and the transmitted 7 bit string, the encoding and decoding accurate positional information processes and the design of SiC navigation syndrome generators are discussed. The visible multilateration method estimates the device’s position by using the MUX signal received from several, non-collinear transmitters. The location and motion information is found by mapping position and estimates the location areas.
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Rajchowski, Piotr, Jacek Stefanski, Jaroslaw Sadowski, and Krzysztof K. Cwalina. "Person Tracking in Ultra-Wide Band Hybrid Localization System Using Reduced Number of Reference Nodes." Sensors 20, no. 7 (April 2, 2020): 1984. http://dx.doi.org/10.3390/s20071984.

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In this article a novel method of positional data integration in an indoor hybrid localization system combining inertial navigation with radio distance measurements is presented. A point of interest is the situation when the positional data and the radio distance measurements are obtained from less than thee reference nodes and it is impossible to unambiguously localize the moving person due to undetermined set of positional equations. The presented method allows to continuously provide localization service even in areas with disturbed propagation of the radio signals. Authors performed simulation and measurement studies of the proposed method to verify the precision of position estimation of a moving person in an indoor environment. It is worth noting that to determine the simulation parameters and realize the experimental studies the hybrid localization system demonstrator was developed, combining inertial navigation and radio distance measurements. In the proposed solution, results of distance measurements taken to less than three reference nodes are used to compensate the drift of the position estimated using the inertial sensor. In the obtained simulation and experimental results it was possible to reduce the localization error by nearly 50% regarding the case when only inertial navigation was used, additionally keeping the long term root mean square error at the level of ca. 0.50 m. That gives a degradation of localization precision below 0.1 m with respect to the fusion Kalman filtration when four reference nodes are present.
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dos Santos, D. R., F. P. Freiman, and N. L. Pavan. "GLOBAL REFINEMENT OF TERRESTRIAL LASER SCANNER DATA REGISTRATION USING WEIGHTED SENSOR POSES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 121–25. http://dx.doi.org/10.5194/isprs-archives-xlii-1-121-2018.

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<p><strong>Abstract.</strong> Terrestrial laser scanner (TLS) sensor captures highly dense and accurate point clouds quite useful for indoor and outdoor mapping, navigation, 3D reconstruction, surveillance, industrial projects, infrastructure management, and others. In this paper, we present a global registration method that weights the sensor poses for refinement of TLS data registration. Our global refinement method assumes that the variance-covariance matrix that describes the uncertainty of sensor poses is available to refine the registration errors. The effectiveness of the proposed method is demonstrated with TLS dataset obtained into outdoor environment. Our results show that the weighting the sensor poses obtained in registration task improves the positional accuracy of TLS sensor.</p>
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Sithole, George, and Sisi Zlatanova. "POSITION, LOCATION, PLACE AND AREA: AN INDOOR PERSPECTIVE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 89–96. http://dx.doi.org/10.5194/isprsannals-iii-4-89-2016.

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Over the last decade, harnessing the commercial potential of smart mobile devices in indoor environments has spurred interest in indoor mapping and navigation. Users experience indoor environments differently. For this reason navigational models have to be designed to adapt to a user’s personality, and to reflect as many cognitive maps as possible. This paper presents an extension of a previously proposed framework. In this extension the notion of placement is accounted for, thereby enabling one aspect of the ‘personalised indoor experience’. In the paper, firstly referential expressions are used as a tool to discuss the different ways of thinking of placement within indoor spaces. Next, placement is expressed in terms of the concept of Position, Location, Place and Area. Finally, the previously proposed framework is extended to include these concepts of placement. An example is provided of the use of the extended framework. &lt;br&gt;&lt;br&gt; Notable characteristics of the framework are: (1) Sub-spaces, resources and agents can simultaneously possess different types of placement, e.g., a person in a room can have an xyz position and a location defined by the room number. While these entities can simultaneously have different forms of placement, only one is dominant. (2) Sub-spaces, resources and agents are capable of possessing modifiers that alter their access and usage. (3) Sub-spaces inherit the modifiers of the resources or agents contained in them. (4) Unlike conventional navigational models which treat resources and obstacles as different types of entities, in the proposed framework there are only resources and whether a resource is an obstacle is determined by a modifier that determines whether a user can access the resource. The power of the framework is that it blends the geometry and topology of space, the influence of human activity within sub-spaces together with the different notions of placement in a way that is simple and yet very flexible.
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Sithole, George, and Sisi Zlatanova. "POSITION, LOCATION, PLACE AND AREA: AN INDOOR PERSPECTIVE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 89–96. http://dx.doi.org/10.5194/isprs-annals-iii-4-89-2016.

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Over the last decade, harnessing the commercial potential of smart mobile devices in indoor environments has spurred interest in indoor mapping and navigation. Users experience indoor environments differently. For this reason navigational models have to be designed to adapt to a user’s personality, and to reflect as many cognitive maps as possible. This paper presents an extension of a previously proposed framework. In this extension the notion of placement is accounted for, thereby enabling one aspect of the ‘personalised indoor experience’. In the paper, firstly referential expressions are used as a tool to discuss the different ways of thinking of placement within indoor spaces. Next, placement is expressed in terms of the concept of Position, Location, Place and Area. Finally, the previously proposed framework is extended to include these concepts of placement. An example is provided of the use of the extended framework. <br><br> Notable characteristics of the framework are: (1) Sub-spaces, resources and agents can simultaneously possess different types of placement, e.g., a person in a room can have an xyz position and a location defined by the room number. While these entities can simultaneously have different forms of placement, only one is dominant. (2) Sub-spaces, resources and agents are capable of possessing modifiers that alter their access and usage. (3) Sub-spaces inherit the modifiers of the resources or agents contained in them. (4) Unlike conventional navigational models which treat resources and obstacles as different types of entities, in the proposed framework there are only resources and whether a resource is an obstacle is determined by a modifier that determines whether a user can access the resource. The power of the framework is that it blends the geometry and topology of space, the influence of human activity within sub-spaces together with the different notions of placement in a way that is simple and yet very flexible.
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Garcia-Fernandez, Miquel, Isaac Hoyas-Ester, Alex Lopez-Cruces, Malgorzata Siutkowska, and Xavier Banqué-Casanovas. "Accuracy in WiFi Access Point Position Estimation Using Round Trip Time." Sensors 21, no. 11 (June 1, 2021): 3828. http://dx.doi.org/10.3390/s21113828.

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WiFi Round Trip Time (RTT) unlocks meter level accuracies in user terminal positions where no other navigation systems, such as Global Navigation Satellite Systems (GNSS), are able to (e.g., indoors). However, little has been done so far to obtain a scalable and automated system that computes the position of the WiFi Access Points (WAP) using RTT and that is able to estimate, in addition to the position, the hardware biases that offset the WiFi ranging measurements. These biases have a direct impact on the ultimate position accuracy of the terminals. This work proposes a method in which the computation of the WiFi Access Points positions and hardware biases (i.e., products) can be estimated based on the ranges and position fixes provided by user terminals (i.e., inverse positioning) and details how this can be improved if raw GNSS measurements (pseudoranges and carrier phase) are also available in the terminal. The data setup used to obtain a performance assessment was configured in a benign scenario (open sky with no obstructions) in order to obtain an upper boundary on the positioning error that can be achieved with the proposed method. Under these conditions, accuracies better than 1.5 m were achieved for the WAP position and hardware bias. The proposed method is suitable to be implemented in an automated manner, without having to rely on dedicated campaigns to survey 802.11mc-compliant WAPs. This paper offers a technique to automatically estimate both mild-indoor WAP products (where terminals have both Wi-Fi RTT and GNSS coverage) and deep-indoor WAP (with no GNSS coverage where the terminals obtain their position exclusively from previously estimated mild-indoor WAPs).
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Schelkshorn, S., and J. Detlefsen. "Position finding using simple Doppler sensors." Advances in Radio Science 5 (June 12, 2007): 153–56. http://dx.doi.org/10.5194/ars-5-153-2007.

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Abstract. An increasing number of modern applications and services is based on the knowledge of the users actual position. Depending on the application a rough position estimate is sufficient, e. g. services in cellular networks that use the information about the users actual cell. Other applications, e. g. navigation systems use the GPS-System for accurate position finding. Beyond these outdoor applications a growing number of indoor applications requires position information. The previously mentioned methods for position finding (mobile cell, GPS) are not usable for these indoor applications. Within this paper we will present a system that relies on the simultaneous measurement of doppler signals at four different positions to obtain position and velocity of an unknown object. It is therefore suiteable for indoor usage, extendig already existing wireless infrastructure.
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Jamil, Faisal, and DoHyeun Kim. "Enhanced Kalman filter algorithm using fuzzy inference for improving position estimation in indoor navigation." Journal of Intelligent & Fuzzy Systems 40, no. 5 (April 22, 2021): 8991–9005. http://dx.doi.org/10.3233/jifs-201352.

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In recent few years, the widespread applications of indoor navigation have compelled the research community to propose novel solutions for detecting objects position in the Indoor environment. Various approaches have been proposed and implemented concerning the indoor positioning systems. This study propose an fuzzy inference based Kalman filter to improve the position estimation in indoor navigation. The presented system is based on FIS based Kalman filter aiming at predicting the actual sensor readings from the available noisy sensor measurements. The proposed approach has two main components, i.e., multi sensor fusion algorithm for positioning estimation and FIS based Kalman filter algorithm. The position estimation module is used to determine the object location in an indoor environment in an accurate way. Similarly, the FIS based Kalman filter is used to control and tune the Kalman filter by considering the previous output as a feedback. The Kalman filter predicts the actual sensor readings from the available noisy readings. To evaluate the proposed approach, the next-generation inertial measurement unit is used to acquire a three-axis gyroscope and accelerometer sensory data. Lastly, the proposed approach’s performance has been investigated considering the MAD, RMSE, and MSE metrics. The obtained results illustrate that the FIS based Kalman filter improve the prediction accuracy against the traditional Kalman filter approach.
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Dissertations / Theses on the topic "Indoor positional navigation"

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Rågmark, Johan. "Calibration and Evaluation of Inertial Navigation with Zero Velocity Update for Industrial Fastening Tools." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298025.

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Indoor Positional Navigation (IPN) systems can be used to track the position of tools in factories which is crucial for quality assurance in many manufacturing industries. Inertial navigation is rarely used on its own because of the noisy Inertial Measurement Unit (IMU) sensors which contribute to large drift. Current IPN systems usually involve the installation and calibration of cameras or antennas, so achieving sufficient accuracy with inertial navigation based IPN would be very desirable. This project aims to evaluate an inertial navigation algorithm, based on Zero Velocity Update (ZUPT), for bolt level positioning by repeatability tests using an industrial robot. The ZUPT algorithm, developed at Atlas Copco, manages to effectively reduce drift and achieve moderate accuracy in position for simpler movements. The gravity tracking Kalman filter dictates the systematic errors in position that grow large with increased degree and dimension of rotation. When keeping rotations within 45◦ for a linear movement the absolute error in position is under 10%. Frequent stops are important when moving in a more complex trajectory to be able to negate drift, consequently detecting the start and stop of motion is crucial. The results show that increased frequency will improve accuracy. It is shown that averaging IMU samples before calculations can increase both truthfulness and precision by 10−25%, if sampling the IMU faster than the calculations. The ZUPT approach of inertial navigation will never yield positional results in real time, and the evaluated algorithm only performs well within certain limitations, mainly frequent stops and simple movements. Despite these limitations there is potential in using the algorithm for quality assurance purposes in hand held industrial fasteners.
Kvalitetssäkring är en central fråga för många tillverkningsindustrier, så som flygplans- och bilindustrin, där det är avgörande att varje förband har dragits åt på rätt sätt för att garantera säkerheten i produkten. Moderna fabriker har centrala styrsystem som kommunicerar med maskiner och verktyg, och ifall något blir fel är det vanligt att fabrikslinan stannar vilket blir kostsamt. Inomhuspositionering (IPS) av hög noggrannhet kan spåra vilken åtdragning som blivit fel, vilket dokumenteras och åtgärdas om möjligt senare, utan att stanna fabrikslinan. Dagens noggranna IPS system för kvalitetssäkring kräver installation och kalibrering av kameror och/eller antenner. Tröghetsnavigering kräver i grunden bara billiga sensorer installerade på verktyget men metoden är mycket opålitlig på grund av sensorernas opålitlighet och brus. I detta projekt har en metod för tröghetsnavigering, användandes Zero Velocity Update (ZUPT), evaluerats för kvalitetssäkring av handhållna verktyg genom repetabilitetstester. Tröghetsnavigeringsalgoritmen som tidigare utvecklats på Atlas Copco lyckas på effektivt sätt reducera drift och uppnår rimlig noggranhet för enklare rörelser. För linjära rörelser med rotationer under 45◦ så erhålls ett absolut positionsfel inom 10%. För att fungera väl även för mer komplexa rörelser krävs frekventa stop, och noggrann rörelsedetektion är central. Denna ZUPT-metod kommer aldrig att kunna generera position i realtid och algoritmen presterar väl endast inom vissa begränsningar. Trots detta så finns god potential för metoden inom kvalitetssäkring för handhållna industriverktyg.
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Yu-ChingMai and 麥耘菁. "Integration of Indoor Position and Navigation using Monocular SLAM and IMU." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/89821323813015095949.

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碩士
國立成功大學
航空太空工程學系
103
GPS (Global Positioning System) dependent positioning and navigation has been developed over recent years, and has been widely used for outdoor positioning and navigation. However, high-rise buildings or indoor environments can block the satellite signal. Therefore, many indoor positioning methods have been developed to respond to this issue. In addition to measuring the distance using sonar and laser, this research uses monocular simultaneous localization and mapping (MonoSLAM) combined with an inertial measurement unit (IMU) to build an indoor positioning system. As time continues to move a vehicle, MonoSLAM measures the distance between the image features and the camera (depth). Making use of the Extend Kalman Filter (EKF), MonoSLAM provides real-time position, velocity and camera attitude. Because the feature points will not always appear and can't be trusted at all times, a wrong estimation will cause the position to diverge. The integrated system in this thesis uses the multi-rate Kalman Filter to complement each method. Finally, the experiment using Virtual Studio C# is shown to measure the MonoSLAM data and IMU data, and Matlab is used to verify the results.
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Francisco, Garcia Nadal Vicent. "Combining WLAN fingerprint-based localization with sensor data for indoor navigation using mobile devices." Master's thesis, 2014. http://hdl.handle.net/10362/13795.

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This project proposes an approach for supporting Indoor Navigation Systems using Pedestrian Dead Reckoning-based methods and by analyzing motion sensor data available in most modern smartphones. Processes suggested in this investigation are able to calculate the distance traveled by a user while he or she is walking. WLAN fingerprint- based navigation systems benefit from the processes followed in this research and results achieved to reduce its workload and improve its positioning estimations.
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Book chapters on the topic "Indoor positional navigation"

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Uchiya, Takahiro, Kiyotaka Sato, and Shinsuke Kajioka. "BLE Beacon Based Indoor Position Estimation Method for Navigation." In Advances in Network-Based Information Systems, 691–99. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98530-5_60.

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Sharp, Ian, and Kegen Yu. "New Methods for Improved Indoor Signal Strength Positioning." In Advances in Wireless Technologies and Telecommunication, 1–49. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3528-7.ch001.

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This chapter focuses on positioning based on received signal strength measurements and radio path-loss modeling. Typically, receiver signal strength from a device such as a smart phone is used to calculate the path-loss and thus estimate distances using a loss model calibrated in an offline process. With known positions and ranges to WiFi access points or simple devices using Bluetooth or Zigbee for data communications, the mobile device position can be estimated. However, due to the complex multipath propagation indoor environment, distance estimation and position determination using current methods are not very accurate. Based on knowledge of the nature of indoor signal propagation and algorithms especially designed for mobile applications, new methods show that positional accuracy of a few meters is possible, even with non-line-of-sight propagation through many intervening walls. Given the current widespread deployment of WiFi indoors, simple software-only solutions are feasible for applications such as general personal navigation and tracking within buildings.
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Gaber, Heba, Mohamed Marey, Safaa Amin, and Mohamed F. Tolba. "Localization and Mapping for Indoor Navigation." In Handbook of Research on Machine Learning Innovations and Trends, 136–60. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2229-4.ch007.

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Mapping and exploration for the purpose of navigation in unknown or partially unknown environments is a challenging problem, especially in indoor environments where GPS signals can't give the required accuracy. This chapter discusses the main aspects for designing a Simultaneous Localization and Mapping (SLAM) system architecture with the ability to function in situations where map information or current positions are initially unknown or partially unknown and where environment modifications are possible. Achieving this capability makes these systems significantly more autonomous and ideal for a large range of applications, especially indoor navigation for humans and for robotic missions. This chapter surveys the existing algorithms and technologies used for localization and mapping and highlights on using SLAM algorithms for indoor navigation. Also the proposed approach for the current research is presented.
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Gaber, Heba, Mohamed Marey, Safaa Amin, and Mohamed F. Tolba. "Localization and Mapping for Indoor Navigation." In Robotic Systems, 930–54. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch046.

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Mapping and exploration for the purpose of navigation in unknown or partially unknown environments is a challenging problem, especially in indoor environments where GPS signals can't give the required accuracy. This chapter discusses the main aspects for designing a Simultaneous Localization and Mapping (SLAM) system architecture with the ability to function in situations where map information or current positions are initially unknown or partially unknown and where environment modifications are possible. Achieving this capability makes these systems significantly more autonomous and ideal for a large range of applications, especially indoor navigation for humans and for robotic missions. This chapter surveys the existing algorithms and technologies used for localization and mapping and highlights on using SLAM algorithms for indoor navigation. Also the proposed approach for the current research is presented.
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Flávio de Melo, Leonimer, Evandro Junior Rodrigues, and João Maurício Rosário. "Wheelchair Secure Navigation with RF Signal Triangulation and Genetic Algorithm Optimization." In Advances in Computational Intelligence and Robotics, 750–92. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7387-8.ch024.

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This chapter describes an approach of secure navigation systems for wheelchairs and mobile robots platforms, using RF signal triangulation and Extended Kalman Filter in conjunction with Genetic Algorithm for indoor trajectory optimization. Initial systems are implemented and tested at virtual environment with conception of supervision and control systems for mobile robots, which are capable of operating and adapting in different environments and conditions. Validation of this environment is made in a nonholonomic mobile robot and in a wheelchair; both used an embedded control rapid prototyping technical for best navigation strategy implementation. ToF (Time-of-Flight) of the RF digital signal interacting with beacons for computational triangulation in the way to provide a pose estimative at bi-dimensional indoor environment, where GPS system is out of range, are depicted. A non-linear filter based on a genetic algorithm as an emerging optimization method to search for optimal positions is described.
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Chen, Liang, Heidi Kuusniemi, Yuwei Chen, Ling Pei, Jingbin Liu, Jian Tang, Laura Ruotsalainen, and Ruizhi Chen. "Information Filter-Assisted Indoor Bluetooth Positioning." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 162–77. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6034-2.ch007.

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This chapter studies wireless positioning using a network of Bluetooth signals. Fingerprints of Received Signal Strength Indicators (RSSI) are used for localization. Due to the relatively long interval between the available consecutive Bluetooth signal strength measurements, the authors applied an information filter method with speed detection, which combines the estimation information from the RSSI measurements with the prior information from the motion model. Speed detection is assisted to correct the outliers of position estimation. The field tests show the effectiveness of the information filter-assisted positioning method, which improves the horizontal positioning accuracy of indoor navigation by about 17% compared to the static fingerprinting positioning method, achieving a 4.2 m positioning accuracy on the average, and about 16% improvement compared to the point Kalman filter. In RSSI fingerprinting localization, building a fingerprint database is usually time-consuming and labour-intensive. In the final section, a self-designed autonomous SLAM robot platform is introduced to be able to carry out the Bluetooth RSS data collecting.
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Flávio de Melo, Leonimer, Felipe Andrade Allemand Borges, and João Maurício Rosário. "Wheelchairs Embedded Control System Design for Secure Navigation With RF Signal Triangulation." In Rapid Automation, 814–49. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8060-7.ch038.

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In the mobile robotic systems a precise estimate of the robot pose (Cartesian [x y] position plus orientation angle θ) with the intention of the path planning optimization is essential for the correct performance, on the part of the robots, for tasks that are destined to it, especially when intention is for mobile robot autonomous navigation. This work uses a ToF (Time-of-Flight) of the RF digital signal interacting with beacons for computational triangulation in the way to provide a pose estimative at bi-dimensional indoor environment, where GPS system is out of range. It's a new technology utilization making good use of old ultrasonic ToF methodology that takes advantage of high performance multicore DSP processors to calculate ToF of the order about ns. A mobile robot platform with differential drive and nonholonomic constraints is used as base for state space, plants and measurements models that are used in the simulations and for validation the experiments. After being tested and validated in the simulator, the control system is programmed in the control board memory of the mobile robot or wheelchair. Thus, the use of material is optimized, firstly validating the entire model virtually and afterwards operating the physical implementation of the navigation system.
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Lim, Jongil, Seokju Lee, Girma Tewolde, and Jaerock Kwon. "Indoor Localization and Navigation for a Mobile Robot Equipped with Rotating Ultrasonic Sensors Using a Smartphone as the Robot's Brain." In Robotic Systems, 1018–29. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch050.

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Identifying the current location of a robot is a prerequisite for robot navigation. To localize a robot, one popular way is to use particle filters that estimate the posterior probabilistic density of a robot's state space. But this Bayesian recursion approach is computationally expensive. Most microcontrollers in a small mobile robot cannot afford it. The authors propose to use a smartphone as a robot's brain in which heavy-duty computations take place whereas an embedded microcontroller on the robot processes rudimentary sensors such as ultrasonic and touch sensors. In their design, a smartphone is wirelessly connected to a robot via Bluetooth by which distance measurements from the robot are sent to the smartphone. Then the smartphone takes responsible for computationally expensive operations like executing the particle filter algorithm. In this paper, the authors designed a mobile robot and its control architecture to demonstrate that the robot can navigate indoor environment while avoiding obstacles and localize its current position.
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Montillet, Jean-Philippe, Kegen Yu, Lukasz Kosma Bonenberg, and Gethin Wyn Roberts. "Optimization Algorithms in Local and Global Positioning." In Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics, 1–53. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9644-0.ch001.

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With the rise of large city and the need of large civil engineering structures and city planning, surveying industry improves continuously their instruments/software in order to get cm accuracy position anywhere. Moreover, since the boom of mobile phones in the late 90s, location has become very valuable information for security, emergency and commercial applications. Depending of the application, the location technologies vary based on the accuracy of the location and the price of the system, which delivers the location information to the user. For outdoor applications, Global Navigation Satellite System is the main candidate, whereas if the user/mobile node is indoors or in a narrow street other technologies will be preferred such as the ones based on Wi-Fi or radio-frequency signal.This chapter provides an overview of different positioning technologies used in geo-location together with their limits/advantages. This chapter studies also a number of algorithms developed to estimate the position coordinates of a static or mobile user or target.
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Conference papers on the topic "Indoor positional navigation"

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Barawkar, Shraddha, Mohammadreza Radmanesh, Manish Kumar, and Kelly Cohen. "Admittance Based Force Control for Collaborative Transportation of a Common Payload Using Two UAVs." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5278.

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This paper presents a novel control approach to perform collaborative transportation by using multiple quadcopter Unmanned Aerial Vehicles (UAVs). In this paper, a leader-follower approach is implemented. The leader UAV uses a Proportional, Integral and Derivative (PID) controller to reach the desired goal point or follow a predefined trajectory. Traditionally, a Position Feedback Controller (PFC) has been used in literature to control the follower UAV. PFC takes the feedback of leader UAVs position to control the follower UAV. Such control schemes work effectively in indoor environments using accurate motion tracking cameras. However, the paper focuses on outdoor applications that requires usage of Global Positioning System (GPS) to receive the positional information of the leader UAV. GPS has inherent errors of order of magnitude that can destabilize the system. The control scheme proposed in this research addresses this major limitation. In this paper, a Force Feedback Controller (FFC) is used to control the follower UAV. An admittance controller is employed to implement this FFC. This controller simulates a virtual spring mass damper system, to generate a desired trajectory for the follower UAV, which complies with the contact forces acting on it. This desired trajectory is then tracked by a traditional PID controller. With the proposed control scheme, the follower UAV can be controlled without using leaders positional feedback and the system can be implemented for real-world applications. The paper presents results of numerical simulations showing the effectiveness of the proposed controller for way-point navigation and complex trajectory tracking.
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"INDOOR NAVIGATION USING APPROXIMATE POSITIONS." In International Conference on Wireless Information Networks and Systems. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0002189301680171.

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Rydell, Joakim, and Erika Emilsson. "CHAMELEON: Visual-inertial indoor navigation." In 2012 IEEE/ION Position, Location and Navigation Symposium - PLANS 2012. IEEE, 2012. http://dx.doi.org/10.1109/plans.2012.6236925.

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Chowaw-Liebman, Ory, Uta Christoph, Karl-Heinz Krempels, and Christoph Terwelp. "Indoor navigation approach based on approximate positions." In 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2010. http://dx.doi.org/10.1109/ipin.2010.5646775.

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Gentner, Christian, Markus Ulmschneider, Isabel Kuehner, and Armin Dammann. "WiFi-RTT Indoor Positioning." In 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS). IEEE, 2020. http://dx.doi.org/10.1109/plans46316.2020.9110232.

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Bekkelien, Anja, and Michel Deriaz. "Harmonization of position providers." In 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2012. http://dx.doi.org/10.1109/ipin.2012.6418855.

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Retscher, Guenther, and Qing Fu. "Continuous indoor navigation with RFID and INS." In 2010 IEEE/ION Position, Location and Navigation Symposium - PLANS 2010. IEEE, 2010. http://dx.doi.org/10.1109/plans.2010.5507242.

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Challamel, Remi, Phillip Tome, Dave Harmer, and Stephane Beauregard. "Performance assessment of indoor location technologies." In 2008 IEEE/ION Position, Location and Navigation Symposium. IEEE, 2008. http://dx.doi.org/10.1109/plans.2008.4569982.

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Imbiriba, Tales, Peng Wu, Gerald LaMountain, Deniz Erdogmus, and Pau Closas. "Recursive Gaussian Processes and Fingerprinting for Indoor Navigation." In 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS). IEEE, 2020. http://dx.doi.org/10.1109/plans46316.2020.9110187.

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Sidorenko, Juri, Norbert Scherer-Negenborn, Michael Arens, and Eckart Michaelsen. "Multilateration of the Local Position Measurement." In 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2016. http://dx.doi.org/10.1109/ipin.2016.7743625.

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