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Indoor Positioning and Navigation

dc.contributor.editorTomažič, Simon
dc.date.accessioned2022-01-11T13:43:57Z
dc.date.available2022-01-11T13:43:57Z
dc.date.issued2021
dc.identifierONIX_20220111_9783036519135_581
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/76846
dc.description.abstractIn recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issuesen_US
dc.subject.classificationthema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilitiesen_US
dc.subject.otherdynamic objects identification and localization
dc.subject.otherlaser cluster
dc.subject.otherradial velocity similarity
dc.subject.otherPearson correlation coefficient
dc.subject.otherparticle filter
dc.subject.othertrilateral indoor positioning
dc.subject.otherRSSI filter
dc.subject.otherRSSI classification
dc.subject.otherstability
dc.subject.otheraccuracy
dc.subject.otherinertial navigation system
dc.subject.otherartificial neural network
dc.subject.othermotion tracking
dc.subject.othersensor fusion
dc.subject.otherindoor navigation system
dc.subject.otherindoor positioning
dc.subject.otherindoor navigation
dc.subject.otherradiating cable
dc.subject.otherleaky feeder
dc.subject.otheraugmented reality
dc.subject.otherBluetooth
dc.subject.otherindoor positioning system
dc.subject.othersmart hospital
dc.subject.otherindoor
dc.subject.otherpositioning
dc.subject.othervisually impaired
dc.subject.otherdeep learning
dc.subject.othermulti-layered perceptron
dc.subject.otherinertial sensor
dc.subject.othersmartphone
dc.subject.othermulti-variational message passing (M-VMP)
dc.subject.otherfactor graph (FG)
dc.subject.othersecond-order Taylor expansion
dc.subject.othercooperative localization
dc.subject.otherjoint estimation of position and clock
dc.subject.otherRTLS
dc.subject.otherindoor positioning system (IPS)
dc.subject.otherposition data
dc.subject.otherindustry 4.0
dc.subject.othertraceability
dc.subject.otherproduct tracking
dc.subject.otherfingerprinting localization
dc.subject.otherBluetooth low energy
dc.subject.otherWi-Fi
dc.subject.otherperformance metrics
dc.subject.otherpositioning algorithms
dc.subject.otherlocation source optimization
dc.subject.otherfuzzy comprehensive evaluation
dc.subject.otherDCPCRLB
dc.subject.otherUAV
dc.subject.otherunmanned aerial vehicles
dc.subject.otherNWPS
dc.subject.otherindoor positioning systems
dc.subject.otherGPS denied
dc.subject.otherGNSS denied
dc.subject.otherautonomous vehicles
dc.subject.othervisible light positioning
dc.subject.othermobile robot
dc.subject.othercalibration
dc.subject.otherappearance-based localization
dc.subject.othercomputer vision
dc.subject.otherGaussian processes
dc.subject.othermanifold learning
dc.subject.otherrobot vision systems
dc.subject.otherimage manifold
dc.subject.otherdescriptor manifold
dc.subject.otherindoor fingerprinting localization
dc.subject.otherGaussian filter
dc.subject.otherKalman filter
dc.subject.otherreceived signal strength indicator
dc.subject.otherchannel state information
dc.subject.otherindoor localization
dc.subject.othervisual-inertial SLAM
dc.subject.otherconstrained optimization
dc.subject.otherpath loss model
dc.subject.otherparticle swarm optimization
dc.subject.otherbeacon
dc.subject.otherabsolute position system
dc.subject.othercooperative algorithm
dc.subject.otherintercepting vehicles
dc.subject.otherrobot framework
dc.subject.otherUWB sensors
dc.subject.otherInternet of Things (IoT)
dc.subject.otherwireless sensor network (WSN)
dc.subject.otherswitched-beam antenna
dc.subject.otherelectronically steerable parasitic array radiator (ESPAR) antenna
dc.subject.otherreceived signal strength (RSS)
dc.subject.otherfingerprinting
dc.subject.otherdown-conversion
dc.subject.otherGPS
dc.subject.othernavigation
dc.subject.otherRF repeaters
dc.subject.otherup-conversion
dc.subject.othern/a
dc.titleIndoor Positioning and Navigation
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-1912-8
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036519135
oapen.relation.isbn9783036519128
oapen.pages350
oapen.place.publicationBasel, Switzerland


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