Pinpointing indoor location with android - not accurate enough

Question

I'm trying to find a solution for pinpointing indoors, specifically inside big crowded places, like malls for example.

I tried building a simple app that tried to pinpoint the phone using only the Network service or Wifi networks.

Usually when Wifi was on the accuracy got to 40-60 meters, and when the wifi was off and only the network service was used, the accuracy got to 600-1000 meters.

Unfortunately that really is too inaccurate for my needs. I read that Google have successfully added an accurate solution for indoors navigation ( http://www.engadget.com/2012/01/08/google-maps-indoor-navigation-las-vegas-ces-2012/ ) , so I guess there might be a possible way to achieve a higher accuracy. 5-10 meters accuracy should suffice.

I'd be happy to get any kind of advice!

Thanks!

Answer 1

The only way to accurately estimate your indoor position is by mapping the signal of known radio hotspots (ie wi-fi). That's why the indoor navigation feature has been implemented just for a few locations.

Answer 2

In highly trafficked public spaces, indoor location solutions tend to be based on WiFi mapping combined with known map information. That approach allows an increase in the accuracy of the location information you will get from your cellular device (over results from testing performed in locations not yet mapped). The actual error rate will depend on how dense and well mapped the WiFi hot spots are, whether they are in stable locations or tend to be moved around, the accuracy of the floor plans, and the effectiveness of the algorithms that integrate the floor plan information (eg, defining which paths may be navigated by people, for example).

Other organizations are deploying infrastructure in public spaces (eg, Nokia - Bluetooth-based). This solution assumes you control the infrastructure (and assumes the latest Bluetooth technology).

I work for TRX Systems and we are complementing these existing technologies with sensor and map fusion technology. In this approach, we fuse information from a multitude of sensors - including signals of opportunity (GPS, WiFi, cell triangulation, Bluetooth), embedded sensors (inertial, altimeter, ranging, compass), and known and inferred map information, to deliver an accurate location indoors.

Carol