05893nam 2201465z- 450 991055774360332120231214133233.0(CKB)5400000000045906(oapen)https://directory.doabooks.org/handle/20.500.12854/76846(EXLCZ)99540000000004590620202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierIndoor Positioning and NavigationBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 electronic resource (350 p.)3-0365-1913-0 3-0365-1912-2 In 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.Technology: general issuesbicsscEnergy industries & utilitiesbicsscdynamic objects identification and localizationlaser clusterradial velocity similarityPearson correlation coefficientparticle filtertrilateral indoor positioningRSSI filterRSSI classificationstabilityaccuracyinertial navigation systemartificial neural networkmotion trackingsensor fusionindoor navigation systemindoor positioningindoor navigationradiating cableleaky feederaugmented realityBluetoothindoor positioning systemsmart hospitalindoorpositioningvisually impaireddeep learningmulti-layered perceptroninertial sensorsmartphonemulti-variational message passing (M-VMP)factor graph (FG)second-order Taylor expansioncooperative localizationjoint estimation of position and clockRTLSindoor positioning system (IPS)position dataindustry 4.0traceabilityproduct trackingfingerprinting localizationBluetooth low energyWi-Fiperformance metricspositioning algorithmslocation source optimizationfuzzy comprehensive evaluationDCPCRLBUAVunmanned aerial vehiclesNWPSindoor positioning systemsGPS deniedGNSS deniedautonomous vehiclesvisible light positioningmobile robotcalibrationappearance-based localizationcomputer visionGaussian processesmanifold learningrobot vision systemsimage manifolddescriptor manifoldindoor fingerprinting localizationGaussian filterKalman filterreceived signal strength indicatorchannel state informationindoor localizationvisual-inertial SLAMconstrained optimizationpath loss modelparticle swarm optimizationbeaconabsolute position systemcooperative algorithmintercepting vehiclesrobot frameworkUWB sensorsInternet of Things (IoT)wireless sensor network (WSN)switched-beam antennaelectronically steerable parasitic array radiator (ESPAR) antennareceived signal strength (RSS)fingerprintingdown-conversionGPSnavigationRF repeatersup-conversionTechnology: general issuesEnergy industries & utilitiesTomažič Simonedt1329483Tomažič SimonothBOOK9910557743603321Indoor Positioning and Navigation3039500UNINA