IndoorNavigation

Indoor navigation refers to determining a device’s position and guiding movement within buildings where satellite navigation signals are unreliable or unavailable. It combines positioning technologies, floor plans, and routing algorithms to provide directions and context-aware information for wayfinding in environments such as malls, airports, hospitals, and office campuses.

Common indoor positioning technologies include radio-based methods such as Wi‑Fi fingerprinting and Bluetooth Low Energy beacons, as well as ultra-wideband for higher precision and RFID for selective tagging. Complementary approaches use inertial measurement units for dead reckoning, magnetometer-based geomagnetic positioning, and computer vision or visual-inertial odometry. Many systems fuse multiple signals with probabilistic filters or machine learning to estimate location and trajectory.

Two broad approaches are fingerprinting, which matches observed signals to a prebuilt location database, and model-based methods such as trilateration, multilateration, or SLAM (simultaneous localization and mapping) that build or refine maps while tracking motion. Accurate maps, floor plans, and landmark features are essential inputs for navigation and routing.

Indoor navigation supports wayfinding, asset tracking, indoor routing for accessibility, and guidance for autonomous robots or drones. It is widely deployed in retail, airports, hospitals, museums, factories, and large office complexes, often integrated with mobile apps, digital signage, or robotics platforms.

Key challenges include achieving robust accuracy across diverse environments, handling multipath signal propagation, energy efficiency, calibration, privacy and data security, and interoperability among devices and platforms. Standards and benchmarks are still evolving to enable widespread adoption. Advances focus on sensor fusion, edge processing, and interoperability to enable scalable indoor positioning.