Indoor navigation system approaches

Navigation systems mainly help users to access unfamiliar environments. Current technologies enable users to use these systems in handheld devices, which effectively increases the popularity of navigation systems and the number of users.

In indoor environments, the GPS cannot provide fair accuracy in tracking. Lack of Global Positioning System (GPS) signals makes navigation more challenging compared to outdoor environments. Radio frequency (RF) signals, computer vision, and sensor-based solutions are more suitable for tracking the users in indoor environments.

The limitation of GPS in indoor navigation systems can be solved by using “high-sensitivity GPS receivers”. However, the cost of implementation can be a barrier to applying this system in real-world scenarios.

The certain applications are wayfinding for humans in airports, railway stations, bus stations, shopping malls, museums, etc. Unlike outdoor areas, navigation through indoor areas is more difficult. The indoor areas contain different types of obstacles, which increases the difficulty of implementing navigation systems.

A human indoor navigation system mainly consists of the following three modules- (1) Indoor positioning, (2) Navigation and (3) Human–machine interaction module. The indoor positioning system estimates the user’s position, the navigation module calculates routes to the destination from current location of the user, and the HMI module helps the user to interact with the system and provide instructions to the user.

In indoor environments, however, all the methods except RSS may fail to estimate the user’s position. The popular RSS-based positioning approaches are trilateration and fingerprinting. RFID technologies are widely implemented in navigation systems because of their simplicity, cost efficiency, and long effective ranges. Wi-Fi-based approaches are implemented in indoor environments, where we have enough Wi-Fi access points, and a dedicated infrastructure is not required. Instead, these approaches can utilize existing building infrastructure because most current buildings will be equipped with Wi-Fi access points. Wi-Fi-based indoor navigation systems make use of RSS fingerprinting or triangulation or trilateration methods for positioning. Bluetooth-based systems have almost similar accuracy as Wi-Fi-based systems and use Bluetooth low energy (BLE) beacons as source of RF signals to track the positions of users using proximity sensing approaches or RSSI fingerprinting. In recent advances, smartphones are usually used as a receiver for both Bluetooth and Wi-Fi signals.

SLAM

The navigation module implements a vision-based SLAM algorithm to construct the map. The SLAM algorithm will extract the image features of the surrounding environment and recreate the path of the camera’s motion.

Simultaneous Localization And Mapping (SLAM) is a technology allowing for constructing and updating a map of an unknown environment while simultaneously keeping track of an agent’s location within it. The app learns how an environment looks, creates a point cloud and re-localizes based on this. Most of the systems only work in smaller areas (200 square meters/2000 square feet) and the environment should not change.

Bluetooth Beacons

Beacons were one of the first innovations to be used for indoor navigation. They are low-power (sometimes they can last a year without recharging or replacing the battery) devices that are used to connect and transmit information between themselves and other devices on the network. They are usually based on Bluetooth technology. When used for indoor navigation, they can detect a smartphone to an accuracy of 2–3 meters.

Beacons are small “senders” of Bluetooth signals, mounted on the wall or ceiling all over your location at 10–20 meters (30–60 feet). Depending on which beacons the device hears and how strong the signal is, it calculates an estimated position. Using beacons, you can estimate a position with an accuracy of 5–10 meters. Currently beacons are used outside of retail in many other industries such as the hospitals, airports, hotels and resorts.

Compass Based Positioning

The compass has massive drifts indoors and is not that ideal for AR. Compass based positioning systems turn this disadvantage into an advantage. They measure the drift of a compass on certain positions and routes and use that for positioning. Users have to walk 10–30 meters to collect compass drift on multiple positions looking for a match with the collected data. Compass based systems provide an accuracy of around 5–10 meters which makes them almost as accurate as Bluetooth beacons, with the disadvantage that the user has to move around for a position. Ideally, you combine Bluetooth beacons with compass based drift.

Markers/QR Codes

Placing markers and saving a location of that marker lets the app know where the user is. For some scenarios it is very handy to use QR codes for indoor positioning since the users need to be aware of the navigation applications and QR code used for app download link. Depending on the size of the marker, users can walk for a distance of 30 meters after filming a QR Code with a drift of 0,5–1,0 meter. This approach is simple, does not require complex installations and will work stable.

Tracking Systems — Comparison Table