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Indoor Positioning and Navigation
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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.
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Keywords
- absolute position system
- accuracy
- appearance-based localization
- artificial neural network
- augmented reality
- autonomous vehicles
- beacon
- Bluetooth
- Bluetooth low energy
- calibration
- channel state information
- Computer vision
- Constrained optimization
- cooperative algorithm
- cooperative localization
- DCPCRLB
- deep learning
- descriptor manifold
- down-conversion
- dynamic objects identification and localization
- Economics, finance, business & management
- electronically steerable parasitic array radiator (ESPAR) antenna
- Energy industries & utilities
- factor graph (FG)
- fingerprinting
- fingerprinting localization
- fuzzy comprehensive evaluation
- Gaussian filter
- Gaussian processes
- GNSS denied
- GPS
- GPS denied
- image manifold
- indoor
- indoor fingerprinting localization
- indoor localization
- indoor navigation
- indoor navigation system
- indoor positioning
- indoor positioning system
- indoor positioning system (IPS)
- indoor positioning systems
- Industry & industrial studies
- Industry 4.0
- inertial navigation system
- inertial sensor
- intercepting vehicles
- Internet of Things (IoT)
- joint estimation of position and clock
- Kalman filter
- laser cluster
- leaky feeder
- location source optimization
- manifold learning
- Mobile Robot
- motion tracking
- multi-layered perceptron
- multi-variational message passing (M-VMP)
- n/a
- Navigation
- NWPS
- Particle Filter
- particle swarm optimization
- path loss model
- Pearson correlation coefficient
- performance metrics
- position data
- positioning
- positioning algorithms
- product tracking
- radial velocity similarity
- radiating cable
- received signal strength (RSS)
- received signal strength indicator
- RF repeaters
- robot framework
- robot vision systems
- RSSI classification
- RSSI filter
- RTLS
- second-order Taylor expansion
- sensor fusion
- smart hospital
- smartphone
- Stability
- switched-beam antenna
- Technology, engineering, agriculture
- Technology: general issues
- thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities
- traceability
- trilateral indoor positioning
- UAV
- unmanned aerial vehicles
- up-conversion
- UWB sensors
- Visible Light Positioning
- visual-inertial SLAM
- visually impaired
- Wi-Fi
- wireless sensor network (WSN)
Links
DOI: 10.3390/books978-3-0365-1912-8Editions