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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 issues --- Energy industries & utilities --- dynamic objects identification and localization --- laser cluster --- radial velocity similarity --- Pearson correlation coefficient --- particle filter --- trilateral indoor positioning --- RSSI filter --- RSSI classification --- stability --- accuracy --- inertial navigation system --- artificial neural network --- motion tracking --- sensor fusion --- indoor navigation system --- indoor positioning --- indoor navigation --- radiating cable --- leaky feeder --- augmented reality --- Bluetooth --- indoor positioning system --- smart hospital --- indoor --- positioning --- visually impaired --- deep learning --- multi-layered perceptron --- inertial sensor --- smartphone --- multi-variational message passing (M-VMP) --- factor graph (FG) --- second-order Taylor expansion --- cooperative localization --- joint estimation of position and clock --- RTLS --- indoor positioning system (IPS) --- position data --- industry 4.0 --- traceability --- product tracking --- fingerprinting localization --- Bluetooth low energy --- Wi-Fi --- performance metrics --- positioning algorithms --- location source optimization --- fuzzy comprehensive evaluation --- DCPCRLB --- UAV --- unmanned aerial vehicles --- NWPS --- indoor positioning systems --- GPS denied --- GNSS denied --- autonomous vehicles --- visible light positioning --- mobile robot --- calibration --- appearance-based localization --- computer vision --- Gaussian processes --- manifold learning --- robot vision systems --- image manifold --- descriptor manifold --- indoor fingerprinting localization --- Gaussian filter --- Kalman filter --- received signal strength indicator --- channel state information --- indoor localization --- visual-inertial SLAM --- constrained optimization --- path loss model --- particle swarm optimization --- beacon --- absolute position system --- cooperative algorithm --- intercepting vehicles --- robot framework --- UWB sensors --- Internet of Things (IoT) --- wireless sensor network (WSN) --- switched-beam antenna --- electronically steerable parasitic array radiator (ESPAR) antenna --- received signal strength (RSS) --- fingerprinting --- down-conversion --- GPS --- navigation --- RF repeaters --- up-conversion --- n/a

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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 issues --- Energy industries & utilities --- dynamic objects identification and localization --- laser cluster --- radial velocity similarity --- Pearson correlation coefficient --- particle filter --- trilateral indoor positioning --- RSSI filter --- RSSI classification --- stability --- accuracy --- inertial navigation system --- artificial neural network --- motion tracking --- sensor fusion --- indoor navigation system --- indoor positioning --- indoor navigation --- radiating cable --- leaky feeder --- augmented reality --- Bluetooth --- indoor positioning system --- smart hospital --- indoor --- positioning --- visually impaired --- deep learning --- multi-layered perceptron --- inertial sensor --- smartphone --- multi-variational message passing (M-VMP) --- factor graph (FG) --- second-order Taylor expansion --- cooperative localization --- joint estimation of position and clock --- RTLS --- indoor positioning system (IPS) --- position data --- industry 4.0 --- traceability --- product tracking --- fingerprinting localization --- Bluetooth low energy --- Wi-Fi --- performance metrics --- positioning algorithms --- location source optimization --- fuzzy comprehensive evaluation --- DCPCRLB --- UAV --- unmanned aerial vehicles --- NWPS --- indoor positioning systems --- GPS denied --- GNSS denied --- autonomous vehicles --- visible light positioning --- mobile robot --- calibration --- appearance-based localization --- computer vision --- Gaussian processes --- manifold learning --- robot vision systems --- image manifold --- descriptor manifold --- indoor fingerprinting localization --- Gaussian filter --- Kalman filter --- received signal strength indicator --- channel state information --- indoor localization --- visual-inertial SLAM --- constrained optimization --- path loss model --- particle swarm optimization --- beacon --- absolute position system --- cooperative algorithm --- intercepting vehicles --- robot framework --- UWB sensors --- Internet of Things (IoT) --- wireless sensor network (WSN) --- switched-beam antenna --- electronically steerable parasitic array radiator (ESPAR) antenna --- received signal strength (RSS) --- fingerprinting --- down-conversion --- GPS --- navigation --- RF repeaters --- up-conversion

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Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods.

Technology: general issues --- filter --- finite memory structure --- infinite memory structure --- smoother --- target tracking --- Indoor Positioning System --- WLAN --- C-Means --- K-Means --- Access Point Selection --- RSS-fingerprint --- smartphone --- pedestrian dead reckoning --- heading estimation --- autoregressive model --- adaptive Kalman filter --- indoor localization --- Wi-Fi received signal strength indicator (RSSI) --- semisupervised learning --- feature extraction --- mobile fingerprinting --- trajectory learning --- localization --- hybrid localization --- Bluetooth Low Energy --- extended kalman filter --- internet of things --- proximity sensors --- smartphone sensors --- pedestrian dead reckoning (PDR) --- Wi-Fi indoor positioning --- sensor fusion frameworks --- Kalman filter --- location fingerprinting --- trilateration --- received signal strength indication (RSSI) --- indoor positioning --- 5G system --- hybrid positioning --- geometric dilution of precision --- closed-form solution --- Cramer-Rao lower bound --- visually impaired (VI) --- computer vision --- deep learning --- multi-label convolutional support vector machine (M-CSVM) --- assistive technology --- visually impaired --- navigational system --- indoor navigation --- markers --- mobile robots --- wireless sensor network --- time of arrival (TOA) --- NLOS --- modified probabilistic data association (MPDA) --- indoor location recognition --- received signal strength (RSS) --- Wi-Fi fingerprint positioning --- deep neural network (DNN) --- optimization methods --- adaptive filter --- hidden Markov models (HMM) --- I/O detection --- GPS signal --- machine learning --- positioning applications. --- PDR --- geomagnetic positioning --- particle filter --- genetic algorithm --- Wi-Fi fine timing measurement --- NLOS identification --- Gaussian model --- carrier phase --- differential pseudolite system --- extended Kalman filter --- reliability --- integrity monitoring --- transparent obstacle recognition --- reflection noise --- laser range finder --- path planning --- mobile robot --- automated data acquisition --- remote sensing technologies --- automated progress reporting --- data fusion --- tracking resources --- bearing estimation --- azimuth estimation --- signal processing --- position estimation --- photodiode array --- indoor ranging algorithm --- channel state information --- received signal strength indicator --- VPR --- fusion navigation --- UWB --- multi-path detection --- NLOS and MP discrimination --- SVM --- random forest --- multilayer perceptron --- LOS --- DWM1000 --- fingerprinting --- smart buildings --- mobile devices --- indoor localization technologies --- model based techniques --- quality control

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods.

Technology: general issues --- filter --- finite memory structure --- infinite memory structure --- smoother --- target tracking --- Indoor Positioning System --- WLAN --- C-Means --- K-Means --- Access Point Selection --- RSS-fingerprint --- smartphone --- pedestrian dead reckoning --- heading estimation --- autoregressive model --- adaptive Kalman filter --- indoor localization --- Wi-Fi received signal strength indicator (RSSI) --- semisupervised learning --- feature extraction --- mobile fingerprinting --- trajectory learning --- localization --- hybrid localization --- Bluetooth Low Energy --- extended kalman filter --- internet of things --- proximity sensors --- smartphone sensors --- pedestrian dead reckoning (PDR) --- Wi-Fi indoor positioning --- sensor fusion frameworks --- Kalman filter --- location fingerprinting --- trilateration --- received signal strength indication (RSSI) --- indoor positioning --- 5G system --- hybrid positioning --- geometric dilution of precision --- closed-form solution --- Cramer-Rao lower bound --- visually impaired (VI) --- computer vision --- deep learning --- multi-label convolutional support vector machine (M-CSVM) --- assistive technology --- visually impaired --- navigational system --- indoor navigation --- markers --- mobile robots --- wireless sensor network --- time of arrival (TOA) --- NLOS --- modified probabilistic data association (MPDA) --- indoor location recognition --- received signal strength (RSS) --- Wi-Fi fingerprint positioning --- deep neural network (DNN) --- optimization methods --- adaptive filter --- hidden Markov models (HMM) --- I/O detection --- GPS signal --- machine learning --- positioning applications. --- PDR --- geomagnetic positioning --- particle filter --- genetic algorithm --- Wi-Fi fine timing measurement --- NLOS identification --- Gaussian model --- carrier phase --- differential pseudolite system --- extended Kalman filter --- reliability --- integrity monitoring --- transparent obstacle recognition --- reflection noise --- laser range finder --- path planning --- mobile robot --- automated data acquisition --- remote sensing technologies --- automated progress reporting --- data fusion --- tracking resources --- bearing estimation --- azimuth estimation --- signal processing --- position estimation --- photodiode array --- indoor ranging algorithm --- channel state information --- received signal strength indicator --- VPR --- fusion navigation --- UWB --- multi-path detection --- NLOS and MP discrimination --- SVM --- random forest --- multilayer perceptron --- LOS --- DWM1000 --- fingerprinting --- smart buildings --- mobile devices --- indoor localization technologies --- model based techniques --- quality control