Last year, we initiated the LoCo project on indoor location. The LoCo page has more information, but our central goal is to provide highly accurate, room-level location information to enable indoor location services to complement the location services built on GPS outdoors.
Last week, we presented our initial results on the work at Ubicomp 2014. In our paper, we introduce a new approach to room-level location based on supervised classification. Specifically, we use boosting in a one-versus-all formulation to enable highly accurate classification based on simple features derived from Wi-Fi received signal strength (RSSI) measures. This approach offloads the bulk of the complexity to an offline training procedure, and the resulting classifier is sufficiently simple to be run on a mobile client directly. We use a simple and robust feature set based on pairwise RSSI margin to both address Wi-Fi RSSI volatility.
The equation above shows an example weak learner which simply looks at two elements in an RSSI scan and compares their difference against a threshold. The final strong classifier for each room is a weighted combination of a set of weak learners greedily selected to discriminate that room. The feature is designed to express the ordering of RSSI values observed for specific access points, and a flexible reliance on the difference between them, and the threshold is determined in training. An additional benefit of this choice is that processing a subset of the RSSI scan according to the selected weak learners further reduces the required computation. Comparing against the kNN matching approach used in RedPin [Bolliger, 2008], our results show competitive performance with substantially reduced complexity. The Table below shows cross validation results from the paper for two data sets collected in our office. The classification time appears in the rightmost column.
We are excited about the early progress we’ve made on this project and look forward to building out our indoor location system in several directions in the near future. But more than that, we look forward to building new location driven applications exploiting this technique which can leverage existing infrastructure (Wi-Fi networks) and devices (cell phones) we already use.