Indoor localization is the basis for many Location Based Services. So far its availability is still sporadic. The industry state-of-the-art, Google Indoor Maps, covers about 10,000 locations world wide, which is only a small fraction of buildings such as shopping malls, train stations, airports, museums, stadiums on the Earth. Two fundamental obstacles exist: 1) mainstream indoor localization technologies rely on RF signatures that require extensive human efforts to measure and periodically re-calibrate; 2) service providers do not have floor plan data and have to go through effort intensive business negotiations to obtain them from various sources. This talk presents two projects targeting the two problems. In Sextant, we leverage environmental physical features that are stable over time thus eliminating the periodic calibration efforts. Users measure their relative locations to these features with smartphones to obtain their location. In Jigsaw, we gather image and inertial data from smartphone users, combine vision and mobile techniques to construct the floor plans with reasonable accuracy. Together they hold the promise to ubiquitous coverage of indoor localization service for the whole planet.