TY - BOOK ID - 146063819 TI - Indoor Positioning and Navigation PY - 2021 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - Technology: general issues KW - Energy industries & utilities KW - dynamic objects identification and localization KW - laser cluster KW - radial velocity similarity KW - Pearson correlation coefficient KW - particle filter KW - trilateral indoor positioning KW - RSSI filter KW - RSSI classification KW - stability KW - accuracy KW - inertial navigation system KW - artificial neural network KW - motion tracking KW - sensor fusion KW - indoor navigation system KW - indoor positioning KW - indoor navigation KW - radiating cable KW - leaky feeder KW - augmented reality KW - Bluetooth KW - indoor positioning system KW - smart hospital KW - indoor KW - positioning KW - visually impaired KW - deep learning KW - multi-layered perceptron KW - inertial sensor KW - smartphone KW - multi-variational message passing (M-VMP) KW - factor graph (FG) KW - second-order Taylor expansion KW - cooperative localization KW - joint estimation of position and clock KW - RTLS KW - indoor positioning system (IPS) KW - position data KW - industry 4.0 KW - traceability KW - product tracking KW - fingerprinting localization KW - Bluetooth low energy KW - Wi-Fi KW - performance metrics KW - positioning algorithms KW - location source optimization KW - fuzzy comprehensive evaluation KW - DCPCRLB KW - UAV KW - unmanned aerial vehicles KW - NWPS KW - indoor positioning systems KW - GPS denied KW - GNSS denied KW - autonomous vehicles KW - visible light positioning KW - mobile robot KW - calibration KW - appearance-based localization KW - computer vision KW - Gaussian processes KW - manifold learning KW - robot vision systems KW - image manifold KW - descriptor manifold KW - indoor fingerprinting localization KW - Gaussian filter KW - Kalman filter KW - received signal strength indicator KW - channel state information KW - indoor localization KW - visual-inertial SLAM KW - constrained optimization KW - path loss model KW - particle swarm optimization KW - beacon KW - absolute position system KW - cooperative algorithm KW - intercepting vehicles KW - robot framework KW - UWB sensors KW - Internet of Things (IoT) KW - wireless sensor network (WSN) KW - switched-beam antenna KW - electronically steerable parasitic array radiator (ESPAR) antenna KW - received signal strength (RSS) KW - fingerprinting KW - down-conversion KW - GPS KW - navigation KW - RF repeaters KW - up-conversion KW - dynamic objects identification and localization KW - laser cluster KW - radial velocity similarity KW - Pearson correlation coefficient KW - particle filter KW - trilateral indoor positioning KW - RSSI filter KW - RSSI classification KW - stability KW - accuracy KW - inertial navigation system KW - artificial neural network KW - motion tracking KW - sensor fusion KW - indoor navigation system KW - indoor positioning KW - indoor navigation KW - radiating cable KW - leaky feeder KW - augmented reality KW - Bluetooth KW - indoor positioning system KW - smart hospital KW - indoor KW - positioning KW - visually impaired KW - deep learning KW - multi-layered perceptron KW - inertial sensor KW - smartphone KW - multi-variational message passing (M-VMP) KW - factor graph (FG) KW - second-order Taylor expansion KW - cooperative localization KW - joint estimation of position and clock KW - RTLS KW - indoor positioning system (IPS) KW - position data KW - industry 4.0 KW - traceability KW - product tracking KW - fingerprinting localization KW - Bluetooth low energy KW - Wi-Fi KW - performance metrics KW - positioning algorithms KW - location source optimization KW - fuzzy comprehensive evaluation KW - DCPCRLB KW - UAV KW - unmanned aerial vehicles KW - NWPS KW - indoor positioning systems KW - GPS denied KW - GNSS denied KW - autonomous vehicles KW - visible light positioning KW - mobile robot KW - calibration KW - appearance-based localization KW - computer vision KW - Gaussian processes KW - manifold learning KW - robot vision systems KW - image manifold KW - descriptor manifold KW - indoor fingerprinting localization KW - Gaussian filter KW - Kalman filter KW - received signal strength indicator KW - channel state information KW - indoor localization KW - visual-inertial SLAM KW - constrained optimization KW - path loss model KW - particle swarm optimization KW - beacon KW - absolute position system KW - cooperative algorithm KW - intercepting vehicles KW - robot framework KW - UWB sensors KW - Internet of Things (IoT) KW - wireless sensor network (WSN) KW - switched-beam antenna KW - electronically steerable parasitic array radiator (ESPAR) antenna KW - received signal strength (RSS) KW - fingerprinting KW - down-conversion KW - GPS KW - navigation KW - RF repeaters KW - up-conversion UR - https://www.unicat.be/uniCat?func=search&query=sysid:146063819 AB - 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. ER -