Geospatial Track: Crowd Learning for Indoor Navigation – Thomas Burgess
indoo.rs enables location based services for indoor applications. With indoo.rs, developers can add new features to their products, including having locations trigger events, track assets, showing closest routes to other places. For this, we use WiFi/beacon radio infrastructure, mobile devices and our cloud which produce lots of geospatial time series data. The real-time indoor navigation fuses independent movement from custom 9D sensor fusion and position estimates obtained by comparing current signal readings to a reference map. This talk will discuss how we create and maintain these maps in our big data machine learning system which leverages crowd data through Kafka and Spark to run SLAM and context aware algorithms to create high quality trajectories. In addition to use in reference maps, these trajectories provide an additional input for our interactive analytics.