Indoor Location's Upcoming SLAM Dunk

Indoor Location's Upcoming SLAM Dunk
There are, of course, a lot of complexities and challenges in implementing a fingerprinting system. But even the best and most sophisticated fingerprinting system requires that fingerprints be gathered every few meters around the site, which can take days for a large sized site.

Consider now another common technology for indoor location position, called sensor fusion. Sensor fusion basically uses sensors that are built into phones, such as accelerometers, gyroscopes, compasses and barometers, to sense the phone's movement and to track the changes in its location by precisely measuring its movement. In other words, if the phone starts out knowing that its at point A, and then measures that it moved one meter south, it should be easy to calculate the new location.

Motion sensing of this sort, however, is notoriously inaccurate. First, the sensors built into today's smartphones are not top-of-the-line professional sensors, they're much smaller and designed for simpler tasks. Second, small amounts of error tend to build up very quickly. If the measured direction of movement is off by just one degree from the actual direction, the location estimate would be very inaccurate after just ten minutes of walking around.

Enter mobile SLAM. The most common approach to mobile SLAM is to combine sensor fusion with fingerprinting. This type of Mobile SLAM system uses sensor fusion to track its location at well as possible when moving around a new site. It also records radio signal fingerprints as it goes. By combining the data from ten or more walks through the site, it can automatically put together a map of the site and a set of fingerprints for locations throughout the site. These fingerprints can then be used for indoor location positioning.

Clearly, there is a lot more to it than this simple description. If sensor fusion is very inaccurate, how can a SLAM system know where the phone is when it's gathering fingerprint data? There are a lot of technology details that different systems used to make this SLAM process work effectively. Some systems assume that the phones will occasionally walk near windows or open areas of a site and receive sporadic GPS signals that will clarify the phone's location. Some systems detect when the phone is at the same place twice, by recognizing the fingerprints, and then review all the movement data collected between the two times at the same location to clarify the path based on the ending point. Some systems detect when two different phones running SLAM are near each other, to clarify the location estimates of both. These and many other innovative approaches are being used to make SLAM work.

Other systems are taking a very different approach to SLAM, using device cameras to implement what I call Visual SLAM. In visual SLAM, real-time images from the phone's camera are analyzed to detect the phone's movement much more accurately than can be done using the phone's other sensors. Also, in addition to collecting radio signal fingerprints, visual SLAM systems collect images around the site, which can also be compared to determine where the phones are, and can be used later for more accurate location positioning. Visual SLAM, however, is often more power-hungry, and cannot be used for a long time without running down a phone's battery.


Tuesday, November 11th 2014
Bruce Krulwich

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