Indoor Positioning and Navigation

Edited by
September 2021
396 pages
  • ISBN978-3-0365-1913-5 (Hardback)
  • ISBN978-3-0365-1912-8 (PDF)

This book is a reprint of the Special Issue Indoor Positioning and Navigation that was published in

Chemistry & Materials Science
Environmental & Earth Sciences

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.

  • Hardback
© 2022 by the authors; CC BY-NC-ND license
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; indoor navigation 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; indoor positioning system; fingerprinting localization; Bluetooth low energy; Wi-Fi; performance metrics; positioning algorithms; cooperative localization; location source optimization; fuzzy comprehensive evaluation; DCPCRLB; UAV; unmanned aerial vehicles; NWPS; indoor positioning systems; GPS denied; GNSS denied; autonomous vehicles; indoor positioning; visible light positioning; sensor fusion; mobile robot; calibration; appearance-based localization; computer vision; Gaussian processes; manifold learning; robot vision systems; indoor positioning; 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; Bluetooth low energy; beacon; absolute position system; cooperative algorithm; intercepting vehicles; indoor positioning; robot framework; UWB sensors; Internet of Things (IoT); wireless sensor network (WSN); switched-beam antenna; electronically steerable parasitic array radiator (ESPAR) antenna; indoor positioning; received signal strength (RSS); fingerprinting; down-conversion; GPS; indoor positioning; navigation; RF repeaters; up-conversion; n/a