While dead reckoning solutions have been deployed for many first responder applications, allowing pedestrians to find their location indoors remains the elusive Holy Grail for location based mobile services. However, progress along many fronts suggests that a solution may be at hand in the near future.
Pedestrian maps are becoming more useful. Not long ago, mobile mapping applications like Google Maps in pedestrian mode often gives the same directions as in vehicle mode except it would ignore one-way traffic restrictions. Now, pedestrian mode direction will take the user to overpasses and underpasses to cross a street. Of course, indoor maps are still emerging and a standard way to handle maps for multi-storied buildings remains lacking. However, proprietary and open source libraries of indoor maps and 3D mirror worlds continue to increase.
Sensor and RF hardware used for position tracking are inexpensive and becoming prevalent in smartphones and tablets. Although the primary location provider on these devices today is GPS, GPS signals are not reliably available. While designers are improving the sensitivity of GPS receivers to make them work better indoors, the mobile industry is also investing in alternatives to GPS for location services.
One alternative to GPS is to use the cellular signals to estimate the users’ distance from nearby cell towers. Using estimated distance from three or more cell towers, users can trilaterate and estimate locations. However, cell phone users, already frustrated by poor coverage and dropped calls, understand that cellular signals are not always reliable either. Signal reflections can also result in large position estimate errors.
Another alternative is to use Wi-Fi fingerprint matching. There are many databases that record the Wi-Fi signatures of physical locations. A program can compare the Wi-Fi signature detected by a smartphone against one of these databases, and make an educated guess on the user’s location. This method is obviously limited by the coverage and accuracy of the fingerprint databases. Accessing the fingerprint databases could also be expensive depending on the users’ data plans.
A third alternative is to use sensors (accelerometers, magnetometers, gyroscopes, and soon, barometers) on smartphones to implement dead reckoning, which is the process of estimating a user’s current position based upon a previously determined position, and then advancing that position based upon known or estimated speeds over elapsed time and course. The advantage of dead reckoning over cell tower trilateration, or Wi-Fi fingerprint matching, or even GPS, is its always-on availability to the user. However, since the new position is calculated solely based on the previous position, the estimation error accumulates over time. Today, dead reckoning solutions are commonly deployed with first responders; for example, to guide a rescuer in a fire scene. In these applications, sensors used for dead reckoning are securely attached to the clothing or footwear of the users. Moving the sensors to a smartphone will require a dead reckoning solution to differentiate between movement of the users’ travel from extraneous and incidental hand motions.
As none of the three alternatives above alone can resolve all the challenges, the final and necessary ingredient to bring pedestrian navigation to fruition is an intelligent algorithm. GPS, cellular signals and Wi-Fi can be used to put an upper limit on the estimation error from dead reckoning, while dead reckoning can fill in the gaps when the other methods are unavailable. While each method by itself can be prone to errors and outages, a smart algorithm can use all the methods together to compensate for the weakness of each other. It can also leverage maps and even camera images to further refine and pin point the user’s location. As these pieces come together, true pedestrian navigation will be at hand.